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14 Commits

Author SHA1 Message Date
62be4c9127 fix(build): exclude task-scoped integration tests from PIT 2026-07-23 00:15:35 +02:00
e7800b29c9 feat!: modularize stemmer models and release infrastructure
Move bundled stemmer dictionaries from the core artifact into independently
versioned model modules. Add model discovery and explicit model-loading APIs,
a standard model aggregate, a model BOM, and dedicated model and catalog
release workflows.

Add full PoliMorf integration, model provenance and licensing validation,
streaming model-input verification, strict dependency verification, consumer
resolution tests, Configuration Cache compatibility, and expanded JMH,
quality, documentation, and release checks.

Upgrade the CycloneDX and JMH Gradle plugins and remove Gradle 10 and Java
compiler deprecations.

BREAKING CHANGE: The core Radixor artifact no longer contains bundled stemmer
dictionaries. Applications must add the required model artifacts, the standard
model aggregate, or model dependencies managed through the Radixor model BOM.
2026-07-22 23:33:28 +02:00
9c5b9e331b fix(build): refresh dependency locks after JMH classpath isolation 2026-07-20 23:50:55 +02:00
05f3855b99 feat(benchmarks): expand multilingual stemming quality evaluation
* cover all Radixor dictionary languages
* add PRIMARY_OUTPUT, ANY_CANDIDATE, and ALL_CANDIDATES policies
* measure pairwise over-stemming and under-stemming
* add balanced accuracy and complementary quality metrics
* compare single-output and multi-output stemmers fairly
* improve result validation, reporting, and documentation
* move stemming quality tests into the standard test source set
* preserve the existing JMH benchmark structure and badge output
2026-07-20 23:20:17 +02:00
6d35f01303 fix: retain only the 10 latest GitHub Pages builds 2026-07-20 01:37:17 +02:00
049f44e697 Add CISTEM and Hunspell benchmarks and refresh results 2026-07-06 01:51:33 +02:00
a52e82933f feat: hunspell benchmarks 2026-07-04 22:38:58 +02:00
5a65de21d9 fix: workflow/benchmark/jmh exceeded the maximum execution time of 30m 2026-07-03 20:03:58 +02:00
3ce9cbc84f chore: update Gradle dependency verification metadata 2026-07-03 18:55:36 +02:00
38620d7e71 feat: prepare Radixor 3.0.0 with contracted tries and compiled patch commands
Introduce contracted compiled patch tries for faster lookup, make compiled
patch commands the primary runtime path, refresh stemmer benchmarks and
documentation, and restructure the documentation for 3.0.0 onboarding.

BREAKING CHANGE: Radixor 3.0.0 promotes compiled patch-command APIs and
new compiled trie artifacts as the primary runtime integration model.
2026-07-03 18:44:39 +02:00
df4552b113 fix(javadoc): Remove collision block 2026-05-24 20:14:28 +02:00
9a84add263 fix(pmd): PMD errors fixed 2026-05-24 20:09:11 +02:00
1a02c41348 feat: Add FrequencyTrie model fingerprints for EGOTHOR v4 analyzer identity
The fingerprint covers trie metadata and the compiled node graph, exposes
a lowercase hex representation plus defensive raw bytes, and is stable
across equivalent trie builds and persistence round-trips.
2026-05-24 20:05:03 +02:00
464b580436 feat: Add FrequencyTrie model fingerprints for EGOTHOR v4 analyzer identity
The fingerprint covers trie metadata and the compiled node graph, exposes
a lowercase hex representation plus defensive raw bytes, and is stable
across equivalent trie builds and persistence round-trips.
2026-05-24 19:54:29 +02:00
308 changed files with 31972 additions and 1411 deletions

2
.gitattributes vendored
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@@ -9,4 +9,4 @@
# Binary files should be left untouched
*.jar binary
*.gz binary

View File

@@ -10,6 +10,8 @@ on:
paths:
- 'src/main/**'
- 'src/jmh/**'
- 'models/**'
- 'build-logic/**'
- 'build.gradle'
- 'gradle.properties'
- 'gradle.lockfile'
@@ -56,7 +58,7 @@ jobs:
test -f gradle/verification-metadata.xml
- name: Run JMH benchmarks
run: ./gradlew clean jmh --no-daemon
run: ./gradlew clean jmh -Pjmh.includes='.*EnglishStemmerComparisonBenchmark.*' --no-daemon
- name: Upload JMH reports
uses: actions/upload-artifact@v4

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@@ -51,7 +51,7 @@ jobs:
test -f gradle/verification-metadata.xml
- name: Execute build, tests, PMD, coverage, Javadoc, distribution packaging, and SBOM generation
run: ./gradlew --no-daemon clean ciRelease distZip pmdMain javadoc jacocoCiReleaseReport cyclonedxBom
run: ./gradlew --no-daemon clean ciRelease distZip pmdMain javadoc jacocoCiReleaseReport :cyclonedxDirectBom
- name: Upload SBOM
if: always()
@@ -156,11 +156,14 @@ jobs:
test -f gradle.properties
test -f gradle/verification-metadata.xml
- name: Validate exact core release tag
run: ./tools/parse-model-release-tag.sh "${GITHUB_REF_NAME}" .
- name: Build release inputs, signed Maven bundle, and SBOM
env:
SIGNING_KEY: ${{ secrets.SIGNING_KEY }}
SIGNING_PASSWORD: ${{ secrets.SIGNING_PASSWORD }}
run: ./gradlew --no-daemon clean ciRelease distZip pmdMain javadoc jacocoCiReleaseReport cyclonedxBom centralBundle
run: ./gradlew --no-daemon clean ciRelease distZip pmdMain javadoc jacocoCiReleaseReport :cyclonedxDirectBom centralBundle
- name: Generate release changelog
shell: bash
@@ -177,24 +180,7 @@ jobs:
shell: bash
env:
CENTRAL_BEARER_TOKEN: ${{ secrets.CENTRAL_BEARER_TOKEN }}
run: |
set -euo pipefail
echo "::add-mask::$CENTRAL_BEARER_TOKEN"
BUNDLE="$(ls build/central-bundle/*.zip)"
HEADER_FILE="$(mktemp)"
trap 'rm -f "$HEADER_FILE"' EXIT
printf 'Authorization: Bearer %s\n' "$CENTRAL_BEARER_TOKEN" > "$HEADER_FILE"
curl \
--fail \
--silent \
--show-error \
--request POST \
--header @"$HEADER_FILE" \
--form "bundle=@${BUNDLE}" \
--form "name=org.egothor:radixor:${GITHUB_REF_NAME#release@}" \
"https://central.sonatype.com/api/v1/publisher/upload?publishingType=AUTOMATIC"
run: ./tools/publish-central-bundle.sh "$(ls build/central-bundle/*.zip)" "org.egothor:radixor:${GITHUB_REF_NAME#release@}"
- name: Publish GitHub release assets
uses: softprops/action-gh-release@v2

37
.github/workflows/catalog-release.yml vendored Normal file
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@@ -0,0 +1,37 @@
name: Model Catalog Release
on:
push:
tags:
- 'models-catalog@*'
permissions:
contents: read
concurrency:
group: model-catalog-${{ github.ref_name }}
cancel-in-progress: false
jobs:
catalog:
runs-on: ubuntu-latest
environment: maven-central
steps:
- uses: actions/checkout@v4
- uses: gradle/actions/wrapper-validation@v4
- uses: actions/setup-java@v4
with:
distribution: temurin
java-version: '21'
- uses: gradle/actions/setup-gradle@v4
- name: Validate catalog tag
run: ./tools/parse-model-release-tag.sh "${GITHUB_REF_NAME}" .
- name: Build only signed catalog metadata
env:
SIGNING_KEY: ${{ secrets.SIGNING_KEY }}
SIGNING_PASSWORD: ${{ secrets.SIGNING_PASSWORD }}
run: ./gradlew --no-daemon verifyModelCatalogReleaseCandidate
- name: Publish only catalog metadata
env:
CENTRAL_BEARER_TOKEN: ${{ secrets.CENTRAL_BEARER_TOKEN }}
run: ./tools/publish-central-bundle.sh "build/model-catalog-release-candidate/radixor-models-catalog-${GITHUB_REF_NAME#models-catalog@}-central-bundle.zip" "org.egothor:radixor-models-catalog:${GITHUB_REF_NAME#models-catalog@}"

147
.github/workflows/model-release.yml vendored Normal file
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@@ -0,0 +1,147 @@
name: Model Release
on:
push:
tags:
- 'model/*@*'
workflow_dispatch:
inputs:
tag:
description: Model tag to validate without publishing
required: true
type: string
permissions:
contents: read
concurrency:
group: model-release-${{ github.event_name == 'push' && github.ref_name || inputs.tag }}
cancel-in-progress: false
jobs:
validate:
name: Validate selected model
runs-on: ubuntu-latest
outputs:
model_id: ${{ steps.release.outputs.MODEL_ID }}
model_version: ${{ steps.release.outputs.MODEL_VERSION }}
gradle_project: ${{ steps.release.outputs.GRADLE_PROJECT }}
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Validate Gradle wrapper
uses: gradle/actions/wrapper-validation@v4
- name: Set up Temurin JDK 21
uses: actions/setup-java@v4
with:
distribution: temurin
java-version: '21'
- name: Set up Gradle caching and instrumentation
uses: gradle/actions/setup-gradle@v4
- name: Verify reproducibility inputs
shell: bash
run: |
set -euo pipefail
test -f gradle.lockfile
test -f gradle.properties
test -f gradle/verification-metadata.xml
- name: Validate and select exactly one model
id: release
shell: bash
env:
REQUESTED_TAG: ${{ inputs.tag }}
run: |
set -euo pipefail
if [[ "${GITHUB_EVENT_NAME}" == "push" ]]; then
tag="${GITHUB_REF_NAME}"
else
tag="${REQUESTED_TAG}"
fi
./tools/parse-model-release-tag.sh "${tag}" . >> "${GITHUB_OUTPUT}"
git merge-base --is-ancestor "${GITHUB_SHA}" origin/main
- name: Validate one model
shell: bash
run: |
set -euo pipefail
project="${{ steps.release.outputs.GRADLE_PROJECT }}"
version="${{ steps.release.outputs.MODEL_VERSION }}"
./gradlew --no-daemon "${project}:clean"
./gradlew --no-daemon "${project}:check"
./gradlew --no-daemon \
"${project}:validateModelRelease" \
-PmodelReleaseVersion="${version}"
publish:
name: Publish selected model
if: github.event_name == 'push'
needs: validate
runs-on: ubuntu-latest
environment: maven-central
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Validate Gradle wrapper
uses: gradle/actions/wrapper-validation@v4
- name: Set up Temurin JDK 21
uses: actions/setup-java@v4
with:
distribution: temurin
java-version: '21'
- name: Set up Gradle caching and instrumentation
uses: gradle/actions/setup-gradle@v4
- name: Verify reproducibility inputs
shell: bash
run: |
set -euo pipefail
test -f gradle.lockfile
test -f gradle.properties
test -f gradle/verification-metadata.xml
- name: Build signed model release candidate
shell: bash
env:
SIGNING_KEY: ${{ secrets.SIGNING_KEY }}
SIGNING_PASSWORD: ${{ secrets.SIGNING_PASSWORD }}
run: |
set -euo pipefail
project="${{ needs.validate.outputs.gradle_project }}"
version="${{ needs.validate.outputs.model_version }}"
./gradlew --no-daemon \
"${project}:packageModelReleaseCandidate" \
-PmodelReleaseVersion="${version}"
- name: Publish one model
shell: bash
env:
CENTRAL_BEARER_TOKEN: ${{ secrets.CENTRAL_BEARER_TOKEN }}
run: |
set -euo pipefail
model_id="${{ needs.validate.outputs.model_id }}"
version="${{ needs.validate.outputs.model_version }}"
./tools/publish-central-bundle.sh \
"models/${model_id}/build/model-release-candidate/central-bundle.zip" \
"org.egothor:radixor-model-${model_id}:${version}"

View File

@@ -10,6 +10,8 @@ on:
- 'src/main/**'
- 'src/test/**'
- 'src/jmh/**'
- 'models/**'
- 'build-logic/**'
- 'build.gradle'
- 'gradle.properties'
- 'gradle.lockfile'
@@ -70,7 +72,7 @@ jobs:
test -f gradle/verification-metadata.xml
- name: Build reports for publication
run: ./gradlew --no-daemon clean ciRelease pmdMain javadoc jacocoCiReleaseReport pitest jmh cyclonedxBom
run: ./gradlew --no-daemon clean ciRelease pmdMain javadoc jacocoCiReleaseReport pitest jmh -Pjmh.includes='.*EnglishStemmerComparisonBenchmark.*' :cyclonedxDirectBom
- name: Prepare gh-pages worktree
shell: bash
@@ -88,6 +90,9 @@ jobs:
cd ..
fi
- name: Prepare staged MkDocs source
run: ./gradlew --no-daemon prepareMkDocsSource verifyModelCatalogDocumentation
- name: Stage published reports
shell: bash
run: |
@@ -191,11 +196,8 @@ jobs:
COVERAGE_BADGE_LATEST_LINK='<li><a href="./builds/latest/metrics/coverage-badge.json">Coverage Badge Metadata</a></li>'
MUTATION_BADGE_LINK='<li><a href="./metrics/pitest-badge.json">Mutation Badge Metadata</a></li>'
MUTATION_BADGE_LATEST_LINK='<li><a href="./builds/latest/metrics/pitest-badge.json">Mutation Badge Metadata</a></li>'
JMH_BADGE_LINK='<li><a href="./metrics/jmh-badge.json">Benchmark Badge Metadata</a></li>'
JMH_BADGE_LATEST_LINK='<li><a href="./builds/latest/metrics/jmh-badge.json">Benchmark Badge Metadata</a></li>'
COVERAGE_BADGE_REPORT_MD='- [Coverage badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/coverage-badge.json)'
MUTATION_BADGE_REPORT_MD='- [Mutation badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/pitest-badge.json)'
JMH_BADGE_REPORT_MD='- [Benchmark badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/jmh-badge.json)'
if [ ! -f "${RUN_METRICS_DIR}/coverage-badge.json" ]; then
COVERAGE_BADGE_LINK='<li>Coverage Badge Metadata: not available</li>'
@@ -207,10 +209,6 @@ jobs:
MUTATION_BADGE_REPORT_MD='- Mutation badge metadata: not currently available'
fi
if [ ! -f "${RUN_METRICS_DIR}/jmh-badge.json" ]; then
JMH_BADGE_REPORT_MD='- Benchmark badge metadata: not currently available'
fi
cat > "${RUN_DIR}/index.html" <<EOF
<!doctype html>
<html lang="en">
@@ -239,7 +237,6 @@ jobs:
${SBOM_XML_LINK:-<li>SBOM (XML): not available</li>}
${COVERAGE_BADGE_LINK}
${MUTATION_BADGE_LINK}
${JMH_BADGE_LINK}
<li><a href="./pitest/">Mutation Testing Report</a></li>
$(
[ "${HAS_JMH}" = "true" ] && { echo "${JMH_TXT_LINK:-<li>Benchmark Results (TXT): not available</li>}"; echo "${JMH_CSV_LINK:-<li>Benchmark Results (CSV): not available</li>}"; } \
@@ -254,7 +251,7 @@ jobs:
cp "${RUN_DIR}/index.html" "${LATEST_DIR}/index.html"
cat > docs/reports.md <<EOF
cat > build/mkdocs-source/reports.md <<EOF
# CI Reports
Radixor publishes durable CI artifacts to GitHub Pages on every qualifying run of \`.github/workflows/pages.yml\`.
@@ -271,24 +268,38 @@ jobs:
${SBOM_JSON_REPORT_MD}
${SBOM_XML_REPORT_MD}
## Benchmark and badge metadata
## Benchmark reports and badge metadata
${JMH_TXT_REPORT_MD}
${JMH_CSV_REPORT_MD}
${COVERAGE_BADGE_REPORT_MD}
${MUTATION_BADGE_REPORT_MD}
${JMH_BADGE_REPORT_MD}
## Historical runs
- [Browse historical build reports](https://leogalambos.github.io/Radixor/builds/)
EOF
# Retain only the 10 most recent numbered builds to stay within
# GitHub Pages capacity limits. The "latest" alias is kept separately.
mapfile -t EXPIRED_BUILDS < <(
find "${SITE_DIR}/builds" -mindepth 1 -maxdepth 1 -type d -printf '%P\n' \
| grep -E '^[0-9]+$' \
| sort -r -n \
| tail -n +11
)
for build in "${EXPIRED_BUILDS[@]}"; do
rm -rf "${SITE_DIR}/builds/${build}"
done
{
echo "# Historical Build Reports"
echo
echo "The following build report sets are currently published on GitHub Pages."
echo
echo "To stay within GitHub Pages capacity limits, only the 10 most recent build report sets are retained."
echo
echo "| Build | Published | Link |"
echo "|---:|---|---|"
@@ -308,19 +319,18 @@ jobs:
| while IFS=$'\t' read -r _ts build published; do
echo "| ${build} | ${published} | [Open](../builds/${build}/) |"
done
} > docs/builds.md
} > build/mkdocs-source/builds.md
- name: Build documentation site (MkDocs Material)
shell: bash
run: |
set -euo pipefail
mkdocs build --strict --site-dir .mkdocs-site
rsync -a --delete --exclude '.git' --exclude '.git/' --exclude 'builds/' .mkdocs-site/ .gh-pages/
mkdocs build --strict --config-file build/mkdocs/mkdocs.yml
rsync -a --delete --exclude '.git' --exclude '.git/' --exclude 'builds/' build/mkdocs-site/ .gh-pages/
mkdir -p .gh-pages/builds
cp .mkdocs-site/builds/index.html .gh-pages/builds/index.html
cp build/mkdocs-site/builds/index.html .gh-pages/builds/index.html
cat > .gh-pages/.nojekyll <<EOF
EOF
rm -rf .mkdocs-site
- name: Commit and push gh-pages
shell: bash

15
.gitignore vendored
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@@ -37,6 +37,7 @@ local.properties
.settings/
.loadpath
.recommenders
.classpath
# External tool builders
.externalToolBuilders/
@@ -94,19 +95,17 @@ local.properties
.jqwik-database
##---------------------------------------------------------------------------------------- Gradle
.gradle
.gradle/
**/build/
!src/**/build/
# MkDocs generated site
/site/
# Ignore Gradle GUI config
gradle-app.setting
# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
# Avoid ignoring the Gradle Wrapper JAR
!gradle-wrapper.jar
# Cache of project
# Gradle task-name cache
.gradletasknamecache
# Ignore Gradle build output directory
build

View File

@@ -2,22 +2,21 @@
<projectDescription>
<name>Radixor</name>
<comment></comment>
<projects>
</projects>
<buildSpec>
<buildCommand>
<name>org.eclipse.jdt.core.javabuilder</name>
<arguments>
</arguments>
</buildCommand>
<buildCommand>
<name>org.eclipse.buildship.core.gradleprojectbuilder</name>
<arguments>
</arguments>
</buildCommand>
</buildSpec>
<projects/>
<natures>
<nature>org.eclipse.jdt.core.javanature</nature>
<nature>org.eclipse.buildship.core.gradleprojectnature</nature>
</natures>
<buildSpec>
<buildCommand>
<name>org.eclipse.jdt.core.javabuilder</name>
<arguments/>
</buildCommand>
<buildCommand>
<name>org.eclipse.buildship.core.gradleprojectbuilder</name>
<arguments/>
</buildCommand>
</buildSpec>
<linkedResources/>
<filteredResources/>
</projectDescription>

View File

@@ -162,7 +162,7 @@
<rule ref="category/java/design.xml/CollapsibleIfStatements"/>
<rule ref="category/java/design.xml/CouplingBetweenObjects">
<properties>
<property name="threshold" value="60" />
<property name="threshold" value="70" />
</properties>
</rule>
<rule ref="category/java/design.xml/CyclomaticComplexity">

110
README.md
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@@ -7,9 +7,8 @@
[![Quality gates](https://github.com/leogalambos/Radixor/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/leogalambos/Radixor/actions/workflows/build.yml)
[![Coverage](https://img.shields.io/endpoint?url=https://leogalambos.github.io/Radixor/builds/latest/metrics/coverage-badge.json)](https://leogalambos.github.io/Radixor/builds/latest/coverage/)
[![Mutation score](https://img.shields.io/endpoint?url=https://leogalambos.github.io/Radixor/builds/latest/metrics/pitest-badge.json)](https://leogalambos.github.io/Radixor/builds/latest/pitest/)
[![English benchmark](https://img.shields.io/endpoint?url=https://leogalambos.github.io/Radixor/builds/latest/metrics/jmh-badge.json)](https://leogalambos.github.io/Radixor/builds/latest/jmh/jmh-results.txt)
*Fast, deterministic, multi-language stemming for Java, built around compact patch-command tries and measured at roughly 4× to 6× the throughput of the Snowball Porter stemmer family on the current English benchmark workload.*
*Deterministic, multi-language stemming for Java, built around compact dictionary-derived patch-command tries with an explicit quality/speed trade-off.*
**Radixor** is a modern multi-language stemming toolkit for Java in the tradition of the original **Egothor** approach. It learns compact word-to-stem transformations from dictionary data, stores them in compiled patch-command tries, and exposes a runtime model designed for speed, determinism, and operational simplicity. Unlike a closed-form dictionary lookup stemmer, Radixor can also generalize beyond explicitly listed word forms.
@@ -23,6 +22,40 @@ It is particularly well suited to systems that need stemming which is:
It also retains the operational advantages of a compiled artifact model: predictable runtime behavior, direct binary loading, and clear separation between preparation-time compilation and live request processing.
## Add Radixor and a model
The core artifact contains the algorithm and registry, but no language dictionary. Add either one minimal model or the optional standard default pack:
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
// Or: runtimeOnly 'org.egothor:radixor-models-standard:<catalog-version>'
}
```
```java
final FrequencyTrie<CompiledPatchCommand> polish =
StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.PL_PL,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
`Language.PL_PL` selects the documented default `pl-pl-unimorph`. The optional `pl-pl-polimorf` model requires its own runtime artifact and explicit selection; adding it does not change the default. See [Model Selection and Loading](docs/model-selection-and-loading.md) for complete executable examples and [Stemmer Models](docs/stemmer-models.md) for artifact concepts.
`radixor-models-standard` is a POM-only runtime aggregate: it brings the 20 default model JARs transitively but publishes no empty aggregate JAR. `radixor-models-bom` is the separate POM-only Maven dependency BOM for version management; importing it alone adds no model. The root CycloneDX SBOM report is unrelated to that dependency BOM.
```java
final FrequencyTrie<CompiledPatchCommand> polimorf =
StemmerPatchTrieLoader.loadCompiled(
"pl-pl-polimorf",
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
Complete PoliMorf construction is supported but unusually memory-intensive: the dedicated verification task uses a 6 GiB maximum heap. Applications should load and retain the resulting immutable trie during startup rather than rebuilding it per request.
## Table of Contents
- [Why Radixor](#why-radixor)
@@ -50,18 +83,33 @@ Radixor is especially attractive when you want something more adaptable than sim
## Performance
Radixor includes a JMH benchmark suite for both its own algorithmic core and a side-by-side English comparison against the Snowball Porter stemmer family.
Radixor performance is best read together with stemming quality. The English dictionary coverage benchmark builds contracted compiled patch tries from deterministic slices of the `US_UK` dictionary and then measures both exact-root agreement and changed-token runtime.
On the current English comparison workload, Radixor with bundled `US_UK` reaches approximately **31 to 32 million tokens per second**. Snowball original Porter reaches approximately **8 million tokens per second**, and Snowball English (Porter2) approximately **5 to 5.5 million tokens per second**.
| Used rows | Actual row ratio | All exact | Changed exact | Root preserved | Speed ms/op | Error ms | ns/token |
| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| 100% | 100.000% | 97.478% | 97.197% | 97.552% | 23.113 | 7.065 | 109.8 |
| 90% | 90.000% | 97.047% | 94.913% | 97.613% | 21.270 | 9.914 | 101.0 |
| 80% | 80.000% | 96.635% | 92.768% | 97.661% | 19.170 | 6.609 | 91.1 |
| 70% | 70.000% | 96.209% | 90.565% | 97.705% | 20.857 | 6.734 | 99.1 |
| 60% | 60.000% | 95.750% | 88.384% | 97.703% | 14.975 | 1.215 | 71.1 |
| 50% | 50.000% | 95.262% | 86.107% | 97.690% | 15.249 | 1.078 | 72.4 |
| 40% | 40.000% | 94.753% | 83.855% | 97.643% | 15.323 | 2.340 | 72.8 |
| 30% | 30.000% | 94.208% | 81.651% | 97.537% | 16.778 | 2.643 | 79.7 |
| 20% | 20.000% | 93.633% | 79.366% | 97.416% | 18.929 | 3.241 | 89.9 |
| 10% | 10.000% | 92.868% | 76.516% | 97.204% | 19.124 | 1.883 | 90.9 |
That places Radixor at approximately:
Column meanings:
- **4× the throughput of Snowball original Porter**
- **6× the throughput of Snowball English (Porter2)**
- `Used rows` is the requested deterministic percentage of English dictionary rows used to build the stemmer.
- `Actual row ratio` is the selected row count divided by the full parsed dictionary row count.
- `All exact` is exact agreement over every word/root pair in the full dictionary.
- `Changed exact` is exact agreement only where the word differs from its root.
- `Root preserved` is the share of already-root forms that remain unchanged.
- `Speed ms/op` is JMH average time for one changed-token benchmark operation.
- `Error ms` is the JMH score error converted to milliseconds.
- `ns/token` is average nanoseconds per changed token in that operation.
on the current benchmark workload.
This is a throughput comparison on the same deterministic token stream. It is **not** a claim that the compared stemmers are linguistically equivalent or interchangeable.
The contracted trie result is materially stronger than the older uncontracted profile: full English coverage reaches 97.478% all-token exactness and 97.197% changed-token exactness at 109.8 ns/token, while even a 10% deterministic dictionary slice remains at 92.868% all-token exactness and 76.516% changed-token exactness at 90.9 ns/token. This is why Radixor benchmark results are documented with both speed and quality instead of a single Porter speed badge.
For benchmark scope, workload design, environment, commands, report locations, and interpretation guidance, see [Benchmarking](docs/benchmarking.md).
@@ -101,6 +149,9 @@ Compared with the historical baseline, Radixor emphasizes:
- **frequency-aware deterministic ordering**
Candidate results are ordered consistently and reproducibly.
- **contracted compiled patch tries**
Uniform patch-command subtrees are collapsed into accepting leaves, reducing hot lookup depth while preserving preferred stemming results.
- **practical subtree reduction modes**
Reduction can be tuned toward stronger compression or more conservative semantic preservation.
@@ -121,7 +172,7 @@ Compared with the historical baseline, Radixor emphasizes:
- Compressed binary persistence
- Programmatic compilation and loading
- CLI compilation tool
- Bundled language resources
- Independently versioned language-model resources
- Support for extending compiled stemmer tables
- Reproducible and auditable engineering posture
@@ -131,11 +182,17 @@ The repository keeps the front page concise and places detailed documentation un
### Getting Started
- [Fast Track](docs/fast-track.md)
The shortest path from adding core plus a model artifact to getting a first stem.
- [Quick Start](docs/quick-start.md)
A practical first guide to loading, compiling, and using Radixor.
A broader developer walkthrough covering loading options, querying, extension, persistence, and metadata.
- [Integration Deep Dive](docs/integration-deep-dive.md)
Dependency setup, model selection, production lifecycle, search-pipeline guidance, and operational checklist.
- [Built-in Languages](docs/built-in-languages.md)
Overview of bundled language resources such as `US_UK`.
Language enum values, default model IDs, artifacts, and optional variants.
- [Dictionary Format](docs/dictionary-format.md)
How to write and normalize stemming dictionaries.
@@ -148,6 +205,9 @@ The repository keeps the front page concise and places detailed documentation un
- [Programmatic Usage Overview](docs/programmatic-usage.md)
Entry point to the Java API and the overall usage model.
- [Model Selection and Loading](docs/model-selection-and-loading.md)
Default, explicit, dual-model, ClassLoader, dependency, and troubleshooting examples.
- [Loading and Building Stemmers](docs/programmatic-loading-and-building.md)
Loading bundled resources, textual dictionaries, binary artifacts, and direct builder usage.
@@ -157,6 +217,9 @@ The repository keeps the front page concise and places detailed documentation un
- [Extending and Persisting Compiled Tries](docs/programmatic-extending-and-persistence.md)
Reopening compiled tries, rebuilding them, and writing binary artifacts.
- [Migration and Backward Compatibility](docs/migration-and-backward-compatibility.md)
Migration from serialized String patch-command application to `CompiledPatchCommand`.
### Concepts and Internals
- [Architecture and Reduction Overview](docs/architecture-and-reduction.md)
@@ -185,7 +248,10 @@ The repository keeps the front page concise and places detailed documentation un
Engineering standards, validation posture, auditability, and operational model.
- [Benchmarking](docs/benchmarking.md)
JMH benchmark methodology, Porter comparison, and result interpretation.
JMH benchmark methodology, dictionary coverage trade-offs, speed, quality, and result interpretation.
- [Benchmark Results](docs/benchmarks/index.md)
Structured reference for methodology, corpora, environment, English coverage, and per-language result pages.
- [Published Reports](docs/reports.md)
Entry points to CI-published reports and GitHub Pages artifacts.
@@ -216,3 +282,19 @@ The goal is to keep the Egothor/Stempel lineage useful as a serious contemporary
## Historical note
Egothor showed that stemming could be both algorithmic and compact. Stempel proved that the approach was practical enough to survive inside major search ecosystems. Radixor continues that tradition with a modernized implementation focused on production use, maintainability, and controlled evolution.
# Radixor 4 artifact architecture
The established `org.egothor:radixor` artifact remains the algorithmic core and contains no language-model data. From version 4 onward, applications explicitly add individual `org.egothor:radixor-model-<model-id>` runtime artifacts or the optional metadata-only `org.egothor:radixor-models-standard` aggregate. Polish defaults to `pl-pl-unimorph`; `pl-pl-polimorf` is opt-in. See [Stemmer Models](docs/stemmer-models.md) and [Migration and Backward Compatibility](docs/migration-and-backward-compatibility.md).
Radixor Java software remains licensed under BSD-3-Clause. UniMorph-derived model data is
distributed under CC BY-SA 3.0, with upstream attribution, the canonical license URI, Radixor
transformations, and Leo Galambos's limited contribution notice carried by each model artifact.
PoliMorf model data retains its separate BSD-2-Clause license. There is no project-wide CC license
directory because the root artifact contains no model data.
```groovy
dependencies {
implementation 'org.egothor:radixor:4.0.0'
runtimeOnly 'org.egothor:radixor-model-pl-pl-polimorf:1.0.0'
}
```

25
build-logic/build.gradle Normal file
View File

@@ -0,0 +1,25 @@
plugins {
id 'groovy-gradle-plugin'
}
dependencies {
testImplementation 'org.junit.jupiter:junit-jupiter:5.14.3'
testRuntimeOnly 'org.junit.platform:junit-platform-launcher:1.14.3'
}
tasks.named('test') {
useJUnitPlatform()
}
gradlePlugin {
plugins {
radixorModel {
id = 'org.egothor.radixor.model'
implementationClass = 'org.egothor.radixor.RadixorModelPlugin'
}
radixorBuildSupport {
id = 'org.egothor.radixor.build-support'
implementationClass = 'org.egothor.radixor.RadixorBuildSupportPlugin'
}
}
}

View File

@@ -0,0 +1,8 @@
rootProject.name = 'radixor-build-logic'
dependencyResolutionManagement {
repositories {
gradlePluginPortal()
mavenCentral()
}
}

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@@ -0,0 +1,21 @@
package org.egothor.radixor
import org.gradle.api.file.ConfigurableFileCollection
import org.gradle.api.tasks.Classpath
import org.gradle.process.CommandLineArgumentProvider
import javax.inject.Inject
abstract class MockitoAgentArgumentProvider implements CommandLineArgumentProvider {
@Classpath
abstract ConfigurableFileCollection getAgentClasspath()
@Inject
MockitoAgentArgumentProvider() {
}
@Override
Iterable<String> asArguments() {
return ["-javaagent:${agentClasspath.singleFile.absolutePath}"]
}
}

View File

@@ -0,0 +1,105 @@
package org.egothor.radixor
import org.gradle.api.DefaultTask
import org.gradle.api.GradleException
import org.gradle.api.file.ConfigurableFileCollection
import org.gradle.api.file.DirectoryProperty
import org.gradle.api.file.RegularFileProperty
import org.gradle.api.provider.MapProperty
import org.gradle.api.provider.Property
import org.gradle.api.tasks.Input
import org.gradle.api.tasks.InputFile
import org.gradle.api.tasks.InputFiles
import org.gradle.api.tasks.OutputDirectory
import org.gradle.api.tasks.PathSensitive
import org.gradle.api.tasks.PathSensitivity
import org.gradle.api.tasks.TaskAction
import java.nio.file.Files
import java.nio.file.Path
import java.nio.file.StandardCopyOption
import java.util.stream.Stream
/** Builds the isolated Maven-layout repository used by consumer resolution tests. */
abstract class PrepareModelConsumerRepositoryTask extends DefaultTask {
@Input abstract Property<String> getCoreVersion()
@Input abstract Property<String> getCatalogVersion()
@Input abstract MapProperty<String, String> getModelVersions()
@InputFile @PathSensitive(PathSensitivity.RELATIVE)
abstract RegularFileProperty getCorePom()
@InputFile @PathSensitive(PathSensitivity.RELATIVE)
abstract RegularFileProperty getCoreJar()
@InputFiles @PathSensitive(PathSensitivity.RELATIVE)
abstract ConfigurableFileCollection getModelPoms()
@InputFiles @PathSensitive(PathSensitivity.RELATIVE)
abstract ConfigurableFileCollection getModelJars()
@InputFile @PathSensitive(PathSensitivity.RELATIVE)
abstract RegularFileProperty getStandardPom()
@InputFile @PathSensitive(PathSensitivity.RELATIVE)
abstract RegularFileProperty getBomPom()
@OutputDirectory
abstract DirectoryProperty getRepositoryDirectory()
/** Creates the repository using only declared task state and Java file APIs. */
@TaskAction
void prepareRepository() {
final Path repository = repositoryDirectory.get().asFile.toPath()
deleteTree(repository)
Files.createDirectories(repository)
install(repository, 'radixor', coreVersion.get(), corePom.get().asFile.toPath(), coreJar.get().asFile.toPath())
final Map<String, Path> pomsByModel = indexModelFiles(modelPoms.files)
final Map<String, Path> jarsByModel = indexModelFiles(modelJars.files)
modelVersions.get().toSorted().each { String modelId, String modelVersion ->
final Path pom = pomsByModel.get(modelId)
final Path jar = jarsByModel.get(modelId)
if (pom == null || jar == null) {
throw new GradleException("Missing generated publication input for model ${modelId}.")
}
PrepareModelConsumerRepositoryTask.install(
repository, "radixor-model-${modelId}", modelVersion, pom, jar)
}
install(repository, 'radixor-models-standard', catalogVersion.get(), standardPom.get().asFile.toPath(), null)
install(repository, 'radixor-models-bom', catalogVersion.get(), bomPom.get().asFile.toPath(), null)
}
private static Map<String, Path> indexModelFiles(final Set<File> files) {
final Map<String, Path> indexed = [:]
files.each { File file ->
Path cursor = file.toPath().toAbsolutePath().parent
while (cursor != null && cursor.fileName.toString() != 'build') cursor = cursor.parent
if (cursor == null || cursor.parent == null) {
throw new GradleException("Cannot determine model ID from generated input ${file}.")
}
final String modelId = cursor.parent.fileName.toString()
if (indexed.put(modelId, file.toPath()) != null) {
throw new GradleException("Duplicate generated publication input for model ${modelId}.")
}
}
return indexed
}
private static void install(final Path repository, final String artifactId, final String version,
final Path pom, final Path jar) {
final Path module = repository.resolve("org/egothor/${artifactId}/${version}")
Files.createDirectories(module)
Files.copy(pom, module.resolve("${artifactId}-${version}.pom"), StandardCopyOption.REPLACE_EXISTING)
if (jar != null) {
Files.copy(jar, module.resolve("${artifactId}-${version}.jar"), StandardCopyOption.REPLACE_EXISTING)
}
}
private static void deleteTree(final Path directory) {
if (!Files.exists(directory)) return
Files.walk(directory).withCloseable { Stream<Path> paths ->
paths.sorted(Comparator.reverseOrder()).forEach(Files::delete)
}
}
}

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@@ -0,0 +1,108 @@
package org.egothor.radixor
import org.gradle.api.DefaultTask
import org.gradle.api.file.DirectoryProperty
import org.gradle.api.file.RegularFileProperty
import org.gradle.api.provider.MapProperty
import org.gradle.api.provider.Property
import org.gradle.api.tasks.Input
import org.gradle.api.tasks.InputFile
import org.gradle.api.tasks.Optional
import org.gradle.api.tasks.OutputDirectory
import org.gradle.api.tasks.PathSensitive
import org.gradle.api.tasks.PathSensitivity
import org.gradle.api.tasks.TaskAction
import java.nio.file.Files
import java.nio.file.Path
import java.nio.file.StandardCopyOption
import java.security.MessageDigest
import java.util.stream.Stream
/** Generates one model's deterministic resource tree without retaining Project state. */
abstract class PrepareModelResourcesTask extends DefaultTask {
@InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getDictionaryFile()
@InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getVersionFile()
@Optional @InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getLicenseFile()
@Optional @InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getNoticeFile()
@Input abstract Property<Boolean> getShareAlike()
@Input abstract MapProperty<String, String> getDescriptorValues()
@OutputDirectory abstract DirectoryProperty getGeneratedDirectory()
/** Copies bounded inputs and writes descriptor and index files. */
@TaskAction
void prepareResources() {
final Path generated = generatedDirectory.get().asFile.toPath()
deleteTree(generated)
final Map<String, String> values = descriptorValues.get()
final String id = values['model.id']
final String resource = "org/egothor/stemmer/models/${id}/stemmer.gz"
final Path dictionaryTarget = generated.resolve(resource)
Files.createDirectories(dictionaryTarget.parent)
Files.copy(dictionaryFile.get().asFile.toPath(), dictionaryTarget, StandardCopyOption.REPLACE_EXISTING)
final Path descriptor = generated.resolve("META-INF/radixor/models/${id}.properties")
Files.createDirectories(descriptor.parent)
Files.writeString(descriptor, descriptorText(values,
versionFile.get().asFile.getText('UTF-8').trim(), resource, sha256(dictionaryFile.get().asFile)))
final Path index = generated.resolve('META-INF/radixor/models.index')
Files.createDirectories(index.parent)
Files.writeString(index, "META-INF/radixor/models/${id}.properties\n")
if (shareAlike.get()) {
final Path notice = generated.resolve("META-INF/NOTICE/${id}-data.txt")
Files.createDirectories(notice.parent)
Files.copy(noticeFile.get().asFile.toPath(), notice, StandardCopyOption.REPLACE_EXISTING)
} else {
final Path license = generated.resolve('META-INF/LICENSES/PoliMorf-BSD-2-Clause.txt')
Files.createDirectories(license.parent)
Files.copy(licenseFile.get().asFile.toPath(), license, StandardCopyOption.REPLACE_EXISTING)
}
}
private static String descriptorText(final Map<String, String> value, final String version,
final String resource, final String checksum) {
return """model.id=${value['model.id']}
model.version=${version}
model.language=${value['model.language']}
model.displayName=${value['model.displayName']}
model.resource=${resource}
model.default=${value['model.default']}
model.format=radixor-dictionary-tsv-gzip
model.formatVersion=1
model.sha256=${checksum}
model.rightToLeft=${['FA_IR', 'HE_IL', 'YI'].contains(value['model.language'])}
model.caseProcessing=LOWERCASE_WITH_LOCALE_ROOT
model.diacriticProcessing=AS_IS
model.storeOriginal=true
source.name=${value['source.name']}
source.version=${value['source.version']}
source.project=${value['source.project']}
source.repository=${value['source.repository']}
source.dataset=${value['source.dataset']}
source.revision=${value['source.revision']}
source.revisionStatus=${value['source.revisionStatus']}
source.license=${value['source.license']}
source.licenseUri=${value['source.licenseUri']}
source.attribution=${value['source.attribution']}
source.verificationDate=${value['source.verificationDate']}
transformations.summary=${value['transformations.summary']}
compiler.radixorVersion=3.x
compiler.radixorCommit=unavailable
statistics.groups=unavailable
statistics.forms=unavailable
"""
}
private static String sha256(final File file) {
return MessageDigest.getInstance('SHA-256').digest(file.bytes)
.collect { byte value -> String.format('%02x', value & 0xff) }.join()
}
private static void deleteTree(final Path directory) {
if (!Files.exists(directory)) return
Files.walk(directory).withCloseable { Stream<Path> paths ->
paths.sorted(Comparator.reverseOrder()).forEach(Files::delete)
}
}
}

View File

@@ -0,0 +1,16 @@
package org.egothor.radixor
import org.gradle.api.Plugin
import org.gradle.api.Project
/** Exposes typed repository build-support tasks to the root build. */
final class RadixorBuildSupportPlugin implements Plugin<Project> {
/** Registers build-support tasks without inspecting project state during execution. */
@Override
void apply(final Project project) {
project.tasks.register('prepareModelConsumerTestRepository', PrepareModelConsumerRepositoryTask) {
group = 'verification'
description = 'Creates an isolated local Maven repository for model dependency-resolution integration tests.'
}
}
}

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@@ -0,0 +1,73 @@
package org.egothor.radixor
import org.gradle.api.model.ObjectFactory
import org.gradle.api.provider.Property
import javax.inject.Inject
/** Declarative configuration for one independently published Radixor model. */
abstract class RadixorModelExtension {
/** Stable model identifier. */
abstract Property<String> getModelId()
/** Radixor language enum constant. */
abstract Property<String> getLanguage()
/** Human-readable model name. */
abstract Property<String> getDisplayName()
/** Whether this is the documented default for its language. */
abstract Property<Boolean> getDefaultModel()
/** Source dictionary name. */
abstract Property<String> getSourceName()
/** Source dictionary version or explicit unavailable marker. */
abstract Property<String> getSourceVersion()
/** Exact upstream revision or the explicit legacy-import sentinel. */
abstract Property<String> getSourceRevision()
/** Upstream source project. */
abstract Property<String> getSourceProject()
/** Official upstream repository URL. */
abstract Property<String> getSourceRepository()
/** Upstream dataset identity. */
abstract Property<String> getSourceDataset()
/** Whether the source revision is recorded or was not recorded by a legacy import. */
abstract Property<String> getSourceRevisionStatus()
/** SPDX license identifier. */
abstract Property<String> getSourceLicense()
/** Canonical URI for the source-data license. */
abstract Property<String> getSourceLicenseUri()
/** Upstream attribution supplied with the source data. */
abstract Property<String> getSourceAttribution()
/** Date on which the upstream metadata was verified. */
abstract Property<String> getSourceVerificationDate()
/** Material transformations applied by Radixor. */
abstract Property<String> getTransformationsSummary()
/** Model-specific data notice input file name, when required. */
abstract Property<String> getNoticeFileName()
/** License input file name. */
abstract Property<String> getLicenseFileName()
/** Creates the extension. */
@Inject
RadixorModelExtension(final ObjectFactory objects) {
defaultModel.convention(false)
sourceVersion.convention('unavailable')
sourceLicense.convention('LicenseRef-Radixor-Stemmer-Data')
licenseFileName.convention('LICENSE-stemmer-data.txt')
noticeFileName.convention('NOTICE-model-data.txt')
}
}

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@@ -0,0 +1,505 @@
package org.egothor.radixor
import org.gradle.api.GradleException
import org.gradle.api.Plugin
import org.gradle.api.Project
import org.gradle.api.file.DuplicatesStrategy
import org.gradle.api.plugins.JavaPlugin
import org.gradle.api.publish.PublishingExtension
import org.gradle.api.publish.maven.MavenPublication
import org.gradle.api.tasks.Copy
import org.gradle.api.tasks.bundling.Jar
import org.gradle.api.tasks.bundling.Zip
import org.gradle.plugins.signing.SigningExtension
import java.nio.charset.CodingErrorAction
import java.nio.charset.StandardCharsets
import java.nio.file.Files
import java.security.MessageDigest
import java.util.zip.GZIPInputStream
/** Configures validation, generation, packaging, and publication for one model artifact. */
final class RadixorModelPlugin implements Plugin<Project> {
/** Applies the model convention to a project. */
@Override
void apply(final Project project) {
project.pluginManager.apply(JavaPlugin)
project.pluginManager.apply('maven-publish')
project.pluginManager.apply('signing')
project.java {
withSourcesJar()
withJavadocJar()
sourceCompatibility = org.gradle.api.JavaVersion.VERSION_21
targetCompatibility = org.gradle.api.JavaVersion.VERSION_21
}
final RadixorModelExtension model = project.extensions.create('radixorModel', RadixorModelExtension)
project.group = 'org.egothor'
project.version = project.providers.gradleProperty('modelReleaseVersion')
.orElse(project.providers.fileContents(project.layout.projectDirectory.file('model-version.txt')).asText.map(String::trim))
.get()
final File input = project.file('src/modelInput/stemmer.gz')
final File generated = project.layout.buildDirectory.dir('generated/modelResources').get().asFile
project.sourceSets.main.resources.setSrcDirs([generated])
final def validate = project.tasks.register('validateModelInput', ValidateModelInputTask) {
group = 'verification'
description = 'Validates the immutable source dictionary, metadata, version, and model-specific licensing material.'
dictionaryFile = project.layout.projectDirectory.file('src/modelInput/stemmer.gz')
versionFile = project.layout.projectDirectory.file('model-version.txt')
modelId = model.modelId
moduleName = project.name
shareAlike = model.sourceLicense.map { String license -> license == 'CC-BY-SA-3.0' }
metadata.put('source.project', model.sourceProject)
metadata.put('source.repository', model.sourceRepository)
metadata.put('source.dataset', model.sourceDataset)
metadata.put('source.revision', model.sourceRevision)
metadata.put('source.revisionStatus', model.sourceRevisionStatus)
metadata.put('source.license', model.sourceLicense)
metadata.put('source.licenseUri', model.sourceLicenseUri)
metadata.put('source.attribution', model.sourceAttribution)
metadata.put('source.verificationDate', model.sourceVerificationDate)
metadata.put('transformations.summary', model.transformationsSummary)
}
final def prepare = project.tasks.register('prepareModelResources', PrepareModelResourcesTask) {
group = 'build'
description = 'Copies validated dictionary bytes and generates the immutable model descriptor and index.'
dependsOn(validate)
dictionaryFile = project.layout.projectDirectory.file('src/modelInput/stemmer.gz')
versionFile = project.layout.projectDirectory.file('model-version.txt')
shareAlike = model.sourceLicense.map { String license -> license == 'CC-BY-SA-3.0' }
generatedDirectory = project.layout.buildDirectory.dir('generated/modelResources')
descriptorValues.put('model.id', model.modelId)
descriptorValues.put('model.language', model.language)
descriptorValues.put('model.displayName', model.displayName)
descriptorValues.put('model.default', model.defaultModel.map(String::valueOf))
descriptorValues.put('source.name', model.sourceName)
descriptorValues.put('source.version', model.sourceVersion)
descriptorValues.put('source.project', model.sourceProject)
descriptorValues.put('source.repository', model.sourceRepository)
descriptorValues.put('source.dataset', model.sourceDataset)
descriptorValues.put('source.revision', model.sourceRevision)
descriptorValues.put('source.revisionStatus', model.sourceRevisionStatus)
descriptorValues.put('source.license', model.sourceLicense)
descriptorValues.put('source.licenseUri', model.sourceLicenseUri)
descriptorValues.put('source.attribution', model.sourceAttribution)
descriptorValues.put('source.verificationDate', model.sourceVerificationDate)
descriptorValues.put('transformations.summary', model.transformationsSummary)
}
project.afterEvaluate {
final boolean shareAlike = model.sourceLicense.get() == 'CC-BY-SA-3.0'
if (shareAlike) {
final def notice = project.layout.projectDirectory.file("src/modelInput/${model.noticeFileName.get()}")
validate.configure { noticeFile = notice }
prepare.configure { noticeFile = notice }
} else {
final def license = project.layout.projectDirectory.file("src/modelInput/${model.licenseFileName.get()}")
validate.configure { licenseFile = license }
prepare.configure { licenseFile = license }
}
}
project.tasks.named('processResources', Copy).configure { dependsOn(prepare); duplicatesStrategy = DuplicatesStrategy.FAIL }
project.tasks.named('sourcesJar', Jar).configure { dependsOn(prepare); exclude('**/stemmer.gz') }
project.tasks.named('javadocJar', Jar).configure { exclude('**/stemmer.gz') }
project.tasks.named('jar', Jar).configure {
archiveBaseName.set("radixor-model-${project.name}")
preserveFileTimestamps = false
reproducibleFileOrder = true
}
final def verifyDescriptor = project.tasks.register('verifyModelDescriptor') {
group = 'verification'; description = 'Verifies generated descriptor identity and checksum.'; dependsOn(prepare)
doLast {
final Properties properties = new Properties()
new File(generated, "META-INF/radixor/models/${model.modelId.get()}.properties").withInputStream(properties::load)
if (properties.getProperty('model.sha256') != sha256(input)) {
throw new GradleException('Generated descriptor checksum does not match the immutable source input.')
}
}
}
final def verifyJar = project.tasks.register('verifyModelJar') {
group = 'verification'; description = 'Verifies the model JAR checksum, layout, metadata, and dictionary-free documentation artifacts.'
dependsOn(project.tasks.named('jar'), project.tasks.named('sourcesJar'), project.tasks.named('javadocJar'))
doLast {
final File archive = project.tasks.named('jar', Jar).get().archiveFile.get().asFile
final List<String> names = []
final String resource = "org/egothor/stemmer/models/${model.modelId.get()}/stemmer.gz"
final boolean shareAlike = model.sourceLicense.get() == 'CC-BY-SA-3.0'
final String licenseResource = 'META-INF/LICENSES/PoliMorf-BSD-2-Clause.txt'
final File sourceLicense = shareAlike ? null : project.file("src/modelInput/${model.licenseFileName.get()}")
final File sourceNotice = shareAlike
? project.file("src/modelInput/${model.noticeFileName.get()}") : null
final String noticeResource = "META-INF/NOTICE/${model.modelId.get()}-data.txt"
String packagedChecksum
String packagedLicenseChecksum
String packagedNoticeChecksum
new java.util.zip.ZipFile(archive).withCloseable { zip ->
zip.entries().each { names.add(it.name) }
final def entry = zip.getEntry(resource)
if (entry != null) {
packagedChecksum = sha256(zip.getInputStream(entry).bytes)
}
final def licenseEntry = zip.getEntry(licenseResource)
if (licenseEntry != null) {
packagedLicenseChecksum = sha256(zip.getInputStream(licenseEntry).bytes)
}
final def noticeEntry = zip.getEntry(noticeResource)
if (noticeEntry != null) {
packagedNoticeChecksum = sha256(zip.getInputStream(noticeEntry).bytes)
}
}
if (names.count { String name -> name.endsWith('/stemmer.gz') } != 1 || !names.contains(resource)) {
throw new GradleException("Model JAR must contain exactly one dictionary at ${resource}.")
}
if (packagedChecksum != sha256(input)) {
throw new GradleException("Packaged dictionary checksum does not match the immutable source input at ${resource}.")
}
if (shareAlike) {
requireMatchingChecksum('notice', noticeResource, sha256(sourceNotice), packagedNoticeChecksum)
validateUniMorphJarContents(names)
} else {
requireMatchingChecksum('license', licenseResource, sha256(sourceLicense), packagedLicenseChecksum)
validatePoliMorfJarContents(names)
}
['META-INF/radixor/models.index', "META-INF/radixor/models/${model.modelId.get()}.properties"].each { String name ->
if (!names.contains(name)) throw new GradleException("Model JAR is missing ${name}.")
}
[project.tasks.named('sourcesJar', Jar).get(), project.tasks.named('javadocJar', Jar).get()].each { Jar task ->
final File documentationArchive = task.archiveFile.get().asFile
new java.util.zip.ZipFile(documentationArchive).withCloseable { zip ->
if (zip.entries().any { entry -> entry.name.endsWith('/stemmer.gz') || entry.name == 'stemmer.gz' }) {
throw new GradleException("Documentation artifact ${documentationArchive.name} must not contain a model dictionary.")
}
}
}
}
}
project.tasks.register('validateModelRelease') {
group = 'verification'; description = 'Validates a tag-supplied model release version.'; dependsOn(verifyDescriptor, verifyJar)
doLast {
if (!project.hasProperty('modelReleaseVersion')) throw new GradleException('Model release validation requires -PmodelReleaseVersion=<version>.')
final String recorded = project.file('model-version.txt').text.trim()
if (project.property('modelReleaseVersion').toString() != recorded) throw new GradleException("Release version does not match model-version.txt: ${recorded}")
}
}
project.tasks.named('check').configure { dependsOn(verifyDescriptor, verifyJar) }
project.extensions.configure(PublishingExtension) { PublishingExtension publishing ->
publishing.publications.create('model', MavenPublication) { MavenPublication publication ->
publication.from(project.components.java)
publication.artifactId = "radixor-model-${project.name}"
publication.pom {
name.set("Radixor model ${project.name}")
description.set(model.displayName.zip(model.sourceLicense) { String displayName, String licenseId ->
final String material = licenseId == 'CC-BY-SA-3.0'
? 'See the packaged model-specific notice.'
: 'See the packaged model-data license.'
return "${displayName}. This artifact contains Radixor-derived model data licensed under ${licenseId}; "
.concat("Radixor software is licensed separately under BSD-3-Clause. ${material}")
})
url.set('https://github.com/leogalambos/Radixor')
licenses {
license {
name.set(model.sourceLicense)
url.set(model.sourceLicenseUri)
distribution.set('repo')
}
}
developers {
developer {
id.set('egothor')
name.set('Leo Galambos')
email.set('egothor@gmail.com')
}
}
scm {
url.set('https://github.com/leogalambos/Radixor')
connection.set('scm:git:https://github.com/leogalambos/Radixor.git')
developerConnection.set('scm:git:ssh://git@github.com/leogalambos/Radixor.git')
}
}
}
publishing.repositories.maven {
name = 'modelStaging'
url = project.layout.buildDirectory.dir('model-staging-repository').get().asFile.toURI()
}
}
final String signingKey = project.providers.environmentVariable('SIGNING_KEY').orNull
final String signingPassword = project.providers.environmentVariable('SIGNING_PASSWORD').orNull
project.extensions.configure(SigningExtension) { SigningExtension signing ->
signing.required = {
project.providers.environmentVariable('GITHUB_REF_TYPE').orNull == 'tag'
}
if (signingKey != null && !signingKey.isBlank()) {
signing.useInMemoryPgpKeys(signingKey, signingPassword)
signing.sign(project.extensions.getByType(PublishingExtension).publications.getByName('model'))
}
}
final def checksums = project.tasks.register('createModelCentralChecksums') {
group = 'publishing'
description = 'Creates Maven Central checksums for this model staging repository.'
dependsOn(project.tasks.named('publishModelPublicationToModelStagingRepository'))
doLast {
final File repository = project.layout.buildDirectory.dir('model-staging-repository').get().asFile
repository.eachFileRecurse { File artifact ->
if (artifact.isFile() && !['.md5', '.sha1', '.sha256', '.sha512'].any {
String extension -> artifact.name.endsWith(extension)
}) {
new File(artifact.absolutePath + '.md5').setText(sha256WithAlgorithm(artifact, 'MD5'), 'US-ASCII')
new File(artifact.absolutePath + '.sha1').setText(sha256WithAlgorithm(artifact, 'SHA-1'), 'US-ASCII')
}
}
}
}
project.tasks.register('packageModelReleaseCandidate', Zip) {
group = 'distribution'
description = 'Packages only this model publication as a Maven-layout local release candidate.'
dependsOn(checksums)
from(project.layout.buildDirectory.dir('model-staging-repository')) {
exclude('**/maven-metadata*.xml*')
}
destinationDirectory.set(project.layout.buildDirectory.dir('model-release-candidate'))
archiveFileName.set('central-bundle.zip')
doFirst {
if (project.providers.environmentVariable('GITHUB_REF_TYPE').orNull == 'tag'
&& (signingKey == null || signingKey.isBlank()
|| signingPassword == null || signingPassword.isBlank())) {
throw new GradleException('A tagged model release requires SIGNING_KEY and SIGNING_PASSWORD.')
}
}
}
}
/** Ensures a required file exists. */
static void requireFile(final File file, final String diagnostic) {
if (!file.isFile()) throw new GradleException(diagnostic)
}
/** Rejects a missing or byte-different packaged licensing resource. */
static void requireMatchingChecksum(final String kind, final String resource,
final String sourceChecksum, final String packagedChecksum) {
if (packagedChecksum != sourceChecksum) {
throw new GradleException("Packaged ${kind} does not match the source ${kind} at ${resource}.")
}
}
/** Validates complete source, licensing, attribution, revision-status, and transformation metadata. */
private static void validateMetadata(final RadixorModelExtension model) {
final Map<String, String> required = [
'source.project': model.sourceProject.orNull,
'source.repository': model.sourceRepository.orNull,
'source.dataset': model.sourceDataset.orNull,
'source.revision': model.sourceRevision.orNull,
'source.revisionStatus': model.sourceRevisionStatus.orNull,
'source.license': model.sourceLicense.orNull,
'source.licenseUri': model.sourceLicenseUri.orNull,
'source.attribution': model.sourceAttribution.orNull,
'source.verificationDate': model.sourceVerificationDate.orNull,
'transformations.summary': model.transformationsSummary.orNull]
required.each { String key, String value ->
if (value == null || value.isBlank()) {
throw new GradleException("Required model metadata is missing: ${key}")
}
}
validateRevisionMetadata(model.sourceRevision.get(), model.sourceRevisionStatus.get())
}
/** Accepts an exact recorded revision or the explicit legacy-import sentinel, but never an absent status. */
static void validateRevisionMetadata(final String revision, final String status) {
if (revision == null || revision.isBlank()) {
throw new GradleException('Required model metadata is missing: source.revision')
}
if (status == null || status.isBlank()) {
throw new GradleException('Required model metadata is missing: source.revisionStatus')
}
final String sentinel = 'not-recorded-in-legacy-import'
if (revision == sentinel && status != sentinel) {
throw new GradleException('The legacy revision sentinel requires source.revisionStatus=not-recorded-in-legacy-import.')
}
if (revision != sentinel && status != 'recorded') {
throw new GradleException('An exact source revision requires source.revisionStatus=recorded.')
}
}
/** Validates the model-specific attribution and ShareAlike notice. */
static void validateShareAlikeNotice(final File notice, final RadixorModelExtension model) {
validateShareAlikeNoticeText(notice.getText('UTF-8'), notice.toString(), model.modelId.get(),
model.sourceRepository.get(), model.sourceLicenseUri.get(), model.sourceRevision.get(),
model.sourceRevisionStatus.get())
}
/** Validates required content in one UniMorph model-data notice. */
static void validateShareAlikeNoticeText(final String text, final String noticeName,
final String modelId, final String repository, final String licenseUri,
final String revision, final String revisionStatus) {
final List<String> required = [
"Model ID: ${modelId}",
"Official repository: ${repository}",
'Attribution:',
'License:\nCreative Commons Attribution-ShareAlike 3.0 Unported',
"Canonical license URI: ${licenseUri}",
'Radixor modifications:',
"Revision status: ${revisionStatus}",
'Copyright (C) 2026, Leo Galambos.',
'Radixor-specific selection, verification, cleaning, normalization,',
'to the extent protected by applicable law.',
'The underlying morphological data remains attributed to UniMorph and',
"This derived model data, including Radixor's protectable contributions,",
'is distributed under Creative Commons Attribution-ShareAlike 3.0',
'Neither UniMorph nor any upstream contributor endorses Radixor.']
if (revision == 'not-recorded-in-legacy-import') {
required.add('The exact UniMorph commit used for the original Radixor import was not recorded.')
}
final List<String> missing = required.findAll { String value -> !text.contains(value) }
if (!missing.isEmpty()) {
throw new GradleException("Model notice ${noticeName} is missing required content: ${missing.join(', ')}")
}
}
/** Rejects generic license files and foreign notices in a UniMorph model artifact. */
static void validateUniMorphJarContents(final List<String> names) {
if (names.any { String name -> name.startsWith('META-INF/LICENSES/') }) {
throw new GradleException('A UniMorph model artifact must use only its model-specific notice for data licensing.')
}
if (names.count { String name -> name.startsWith('META-INF/NOTICE/') && !name.endsWith('/') } != 1) {
throw new GradleException('A UniMorph model artifact must contain exactly one model-specific notice.')
}
}
/** Rejects UniMorph licensing material in the separately licensed PoliMorf artifact. */
static void validatePoliMorfJarContents(final List<String> names) {
if (names.any { String name -> name.startsWith('META-INF/NOTICE/')
|| name.contains('CC-BY-SA') }) {
throw new GradleException('The PoliMorf artifact must not contain UniMorph CC BY-SA material.')
}
}
/** Memory-bounded validation statistics for one dictionary input. */
static final class DictionaryValidationResult {
final long acceptedGroupCount
final long acceptedFormCount
final long ignoredEmptyVariantCount
DictionaryValidationResult(final long acceptedGroupCount, final long acceptedFormCount,
final long ignoredEmptyVariantCount) {
this.acceptedGroupCount = acceptedGroupCount
this.acceptedFormCount = acceptedFormCount
this.ignoredEmptyVariantCount = ignoredEmptyVariantCount
}
}
/** Validates GZip, strict UTF-8, and dictionary rows without retaining decompressed input. */
static DictionaryValidationResult validateDictionary(final File file) {
long acceptedGroups = 0L
long acceptedForms = 0L
long ignoredEmptyVariants = 0L
try {
final def decoder = StandardCharsets.UTF_8.newDecoder()
.onMalformedInput(CodingErrorAction.REPORT)
.onUnmappableCharacter(CodingErrorAction.REPORT)
Files.newInputStream(file.toPath()).withCloseable { InputStream source ->
new BufferedInputStream(source).withCloseable { BufferedInputStream bufferedInput ->
new GZIPInputStream(bufferedInput).withCloseable { GZIPInputStream gzipInput ->
new BufferedReader(new InputStreamReader(gzipInput, decoder)).withCloseable { BufferedReader reader ->
String line
long lineNumber = 0L
while ((line = reader.readLine()) != null) {
lineNumber++
final String trimmed = line.trim()
if (trimmed && !trimmed.startsWith('#') && !trimmed.startsWith('//')) {
final String[] columns = line.split('\\t', -1)
if (columns[0].isEmpty()) {
throw new GradleException("Invalid Radixor dictionary row ${lineNumber} in ${file}.")
}
if (containsUnicodeWhitespace(columns[0])) continue
long acceptedRowForms = 1L
for (int index = 1; index < columns.length; index++) {
final String variant = columns[index]
if (variant.isEmpty()) {
ignoredEmptyVariants++
} else if (!containsUnicodeWhitespace(variant)) {
acceptedRowForms++
}
}
acceptedGroups++
acceptedForms += acceptedRowForms
}
}
}
}
}
}
} catch (GradleException exception) {
throw exception
} catch (Exception exception) {
throw new GradleException("Invalid GZip or UTF-8 model input: ${file}", exception)
}
if (acceptedGroups == 0L) throw new GradleException("Model dictionary contains no valid rows: ${file}")
if (ignoredEmptyVariants > 0L) {
println("Model validation warning: " + file + " contains " + ignoredEmptyVariants
+ " empty variant columns; the production parser intentionally ignores empty variants.")
}
return new DictionaryValidationResult(acceptedGroups, acceptedForms, ignoredEmptyVariants)
}
/** Detects Unicode whitespace in one bounded dictionary field. */
private static boolean containsUnicodeWhitespace(final String value) {
for (int index = 0; index < value.length(); index++) {
if (Character.isWhitespace(value.charAt(index))) return true
}
return false
}
/** Builds deterministic descriptor text. */
private static String descriptorText(final RadixorModelExtension model, final String version,
final String resource, final String checksum) {
return """model.id=${model.modelId.get()}
model.version=${version}
model.language=${model.language.get()}
model.displayName=${model.displayName.get()}
model.resource=${resource}
model.default=${model.defaultModel.get()}
model.format=radixor-dictionary-tsv-gzip
model.formatVersion=1
model.sha256=${checksum}
model.rightToLeft=${['FA_IR', 'HE_IL', 'YI'].contains(model.language.get())}
model.caseProcessing=LOWERCASE_WITH_LOCALE_ROOT
model.diacriticProcessing=AS_IS
model.storeOriginal=true
source.name=${model.sourceName.get()}
source.version=${model.sourceVersion.get()}
source.project=${model.sourceProject.get()}
source.repository=${model.sourceRepository.get()}
source.dataset=${model.sourceDataset.get()}
source.revision=${model.sourceRevision.get()}
source.revisionStatus=${model.sourceRevisionStatus.get()}
source.license=${model.sourceLicense.get()}
source.licenseUri=${model.sourceLicenseUri.get()}
source.attribution=${model.sourceAttribution.get()}
source.verificationDate=${model.sourceVerificationDate.get()}
transformations.summary=${model.transformationsSummary.get()}
compiler.radixorVersion=3.x
compiler.radixorCommit=unavailable
statistics.groups=unavailable
statistics.forms=unavailable
"""
}
/** Calculates the lowercase hexadecimal SHA-256 digest. */
private static String sha256(final File file) {
return sha256(file.bytes)
}
/** Calculates the lowercase hexadecimal SHA-256 digest of bytes. */
private static String sha256(final byte[] bytes) {
return MessageDigest.getInstance('SHA-256').digest(bytes).collect { byte value -> String.format('%02x', value & 0xff) }.join()
}
/** Calculates a lowercase hexadecimal digest using the requested algorithm. */
private static String sha256WithAlgorithm(final File file, final String algorithm) {
return MessageDigest.getInstance(algorithm).digest(file.bytes)
.collect { byte value -> String.format('%02x', value & 0xff) }.join()
}
}

View File

@@ -0,0 +1,57 @@
package org.egothor.radixor
import org.gradle.api.DefaultTask
import org.gradle.api.GradleException
import org.gradle.api.file.RegularFileProperty
import org.gradle.api.provider.MapProperty
import org.gradle.api.provider.Property
import org.gradle.api.tasks.Input
import org.gradle.api.tasks.InputFile
import org.gradle.api.tasks.Optional
import org.gradle.api.tasks.PathSensitive
import org.gradle.api.tasks.PathSensitivity
import org.gradle.api.tasks.TaskAction
/** Validates one immutable model input without retaining Project state. */
abstract class ValidateModelInputTask extends DefaultTask {
@InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getDictionaryFile()
@InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getVersionFile()
@Optional @InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getLicenseFile()
@Optional @InputFile @PathSensitive(PathSensitivity.RELATIVE) abstract RegularFileProperty getNoticeFile()
@Input abstract Property<String> getModelId()
@Input abstract Property<String> getModuleName()
@Input abstract Property<Boolean> getShareAlike()
@Input abstract MapProperty<String, String> getMetadata()
/** Performs deterministic metadata, licensing, and streaming dictionary validation. */
@TaskAction
void validateInput() {
final File dictionary = dictionaryFile.get().asFile
final String id = modelId.get()
final String version = versionFile.get().asFile.getText('UTF-8').trim()
if (id != moduleName.get() || !(id ==~ /[a-z]{2}(?:-[a-z]{2})?-[a-z0-9]+(?:-[a-z0-9]+)*/)) {
throw new GradleException("Model ID '${id}' must equal module '${moduleName.get()}' and use the safe model-ID syntax.")
}
if (!(version ==~ /[0-9]+\.[0-9]+\.[0-9]+(?:[-+][0-9A-Za-z.-]+)?/)) {
throw new GradleException("Invalid semantic model version '${version}'.")
}
final Map<String, String> values = metadata.get()
values.each { String key, String value ->
if (value == null || value.isBlank()) throw new GradleException("Required model metadata is missing: ${key}")
}
RadixorModelPlugin.validateRevisionMetadata(values['source.revision'], values['source.revisionStatus'])
if (shareAlike.get()) {
final File notice = noticeFile.get().asFile
RadixorModelPlugin.validateShareAlikeNoticeText(notice.getText('UTF-8'), notice.toString(), id,
values['source.repository'], values['source.licenseUri'], values['source.revision'],
values['source.revisionStatus'])
} else {
final String text = licenseFile.get().asFile.getText('UTF-8')
if (!text.contains('SPDX-License-Identifier: BSD-2-Clause')
|| !text.contains('Copyright (c) 2016, Marcin Miłkowski')) {
throw new GradleException('The PoliMorf license must contain the complete BSD-2-Clause text and upstream attribution.')
}
}
RadixorModelPlugin.validateDictionary(dictionary)
}
}

View File

@@ -0,0 +1,205 @@
package org.egothor.radixor
import org.gradle.api.GradleException
import org.junit.jupiter.api.Test
import org.junit.jupiter.api.io.TempDir
import java.nio.charset.StandardCharsets
import java.nio.file.Files
import java.nio.file.Path
import java.util.zip.GZIPOutputStream
import static org.junit.jupiter.api.Assertions.assertEquals
import static org.junit.jupiter.api.Assertions.assertThrows
import static org.junit.jupiter.api.Assertions.assertTrue
/** Tests model licensing metadata and packaged-resource validation boundaries. */
final class RadixorModelPluginTest {
@TempDir
Path temporaryDirectory
/** Accepts a known exact source revision. */
@Test
void acceptsKnownExactRevision() {
RadixorModelPlugin.validateRevisionMetadata('6e63b53', 'recorded')
}
/** Accepts the explicit legacy-import sentinel without fabricating a revision. */
@Test
void acceptsUnknownLegacyRevision() {
RadixorModelPlugin.validateRevisionMetadata(
'not-recorded-in-legacy-import', 'not-recorded-in-legacy-import')
}
/** Rejects a missing revision-status declaration. */
@Test
void rejectsMissingRevisionStatus() {
assertThrows(GradleException) {
RadixorModelPlugin.validateRevisionMetadata('6e63b53', '')
}
}
/** Rejects a missing model-specific notice input. */
@Test
void rejectsMissingLicensingInputs() {
File missing = new File('build/nonexistent-model-licensing-input')
assertThrows(GradleException) {
RadixorModelPlugin.requireFile(missing, 'Required model notice is missing')
}
}
/** Accepts a complete model-specific UniMorph notice. */
@Test
void acceptsCompleteUniMorphNotice() {
validateNotice(validNotice())
}
/** Rejects each independently required notice statement. */
@Test
void rejectsIncompleteUniMorphNotices() {
[
'Copyright (C) 2026, Leo Galambos.',
'Attribution:',
'Creative Commons Attribution-ShareAlike 3.0 Unported',
'Canonical license URI:',
"This derived model data, including Radixor's protectable contributions,",
'Radixor modifications:',
'Revision status:',
'Neither UniMorph nor any upstream contributor endorses Radixor.'
].each { String required ->
assertThrows(GradleException) {
validateNotice(validNotice().replace(required, 'omitted'))
}
}
}
/** Rejects packaged notice bytes that differ from their model-module source. */
@Test
void rejectsIncorrectPackagedNotice() {
assertThrows(GradleException) {
RadixorModelPlugin.requireMatchingChecksum(
'notice', 'META-INF/NOTICE/test-model-data.txt', 'source', 'different')
}
}
/** Rejects UniMorph CC material in the separately licensed PoliMorf artifact. */
@Test
void rejectsUniMorphMaterialInPoliMorf() {
assertThrows(GradleException) {
RadixorModelPlugin.validatePoliMorfJarContents(
['META-INF/LICENSES/PoliMorf-BSD-2-Clause.txt', 'META-INF/NOTICE/test-data.txt'])
}
assertThrows(GradleException) {
RadixorModelPlugin.validatePoliMorfJarContents(
['META-INF/LICENSES/PoliMorf-BSD-2-Clause.txt', 'META-INF/LICENSES/CC-BY-SA-3.0.txt'])
}
}
/** Streams a large dictionary while retaining only aggregate counters and the current row. */
@Test
void validatesLargeDictionaryWithBoundedState() {
final int groups = 250_000
final File dictionary = temporaryDirectory.resolve('large.gz').toFile()
writeGzip(dictionary) { BufferedWriter writer ->
for (int index = 0; index < groups; index++) {
writer.write("stem${index}\tvariant${index}\t\n")
}
}
final RadixorModelPlugin.DictionaryValidationResult result =
RadixorModelPlugin.validateDictionary(dictionary)
assertEquals(groups, result.acceptedGroupCount)
assertEquals(groups * 2L, result.acceptedFormCount)
assertEquals(groups, result.ignoredEmptyVariantCount)
}
/** Rejects a source that is not a GZip stream. */
@Test
void rejectsInvalidGzip() {
final File dictionary = temporaryDirectory.resolve('invalid.gz').toFile()
Files.writeString(dictionary.toPath(), 'not gzip', StandardCharsets.UTF_8)
assertThrows(GradleException) { RadixorModelPlugin.validateDictionary(dictionary) }
}
/** Rejects malformed UTF-8 through the strict incremental decoder. */
@Test
void rejectsMalformedUtf8() {
final File dictionary = temporaryDirectory.resolve('malformed-utf8.gz').toFile()
new GZIPOutputStream(Files.newOutputStream(dictionary.toPath())).withCloseable { OutputStream output ->
output.write([0x73, 0x74, 0x65, 0x6d, 0x09, 0xc3, 0x28, 0x0a] as byte[])
}
assertThrows(GradleException) { RadixorModelPlugin.validateDictionary(dictionary) }
}
/** Rejects structurally invalid rows with an empty stem. */
@Test
void rejectsInvalidRows() {
final File dictionary = temporaryDirectory.resolve('invalid-row.gz').toFile()
writeGzip(dictionary) { BufferedWriter writer -> writer.write("\tvariant\n") }
assertThrows(GradleException) { RadixorModelPlugin.validateDictionary(dictionary) }
}
/** Preserves the production parser policy for Unicode-whitespace items. */
@Test
void rejectsUnicodeWhitespaceItemsWithoutRejectingTheSource() {
final File dictionary = temporaryDirectory.resolve('whitespace-items.gz').toFile()
writeGzip(dictionary) { BufferedWriter writer ->
writer.write("invalid stem\tvariant\n")
writer.write("valid\taccepted\tinvalid variant\n")
}
final RadixorModelPlugin.DictionaryValidationResult result =
RadixorModelPlugin.validateDictionary(dictionary)
assertEquals(1L, result.acceptedGroupCount)
assertEquals(2L, result.acceptedFormCount)
}
/** Streams the complete maintained PoliMorf model input successfully. */
@Test
void validatesFullPoliMorfInput() {
final List<File> candidates = [
new File('models/pl-pl-polimorf/src/modelInput/stemmer.gz'),
new File('../models/pl-pl-polimorf/src/modelInput/stemmer.gz')]
final File dictionary = candidates.find { File candidate -> candidate.isFile() }
assertTrue(dictionary != null, 'The complete PoliMorf model input must be available to build-logic tests.')
final RadixorModelPlugin.DictionaryValidationResult result =
RadixorModelPlugin.validateDictionary(dictionary)
assertTrue(result.acceptedGroupCount > 0L)
assertTrue(result.acceptedFormCount > result.acceptedGroupCount)
}
private static void writeGzip(final File target, final Closure<Void> content) {
new GZIPOutputStream(Files.newOutputStream(target.toPath())).withCloseable { OutputStream gzip ->
new BufferedWriter(new OutputStreamWriter(gzip, StandardCharsets.UTF_8)).withCloseable {
BufferedWriter writer -> content.call(writer)
}
}
}
private static void validateNotice(final String text) {
RadixorModelPlugin.validateShareAlikeNoticeText(text, 'test notice', 'test-model',
'https://github.com/unimorph/test', 'https://creativecommons.org/licenses/by-sa/3.0/',
'not-recorded-in-legacy-import', 'not-recorded-in-legacy-import')
}
private static String validNotice() {
return '''Model ID: test-model
Official repository: https://github.com/unimorph/test
Attribution: UniMorph and upstream contributors
License:
Creative Commons Attribution-ShareAlike 3.0 Unported
Canonical license URI: https://creativecommons.org/licenses/by-sa/3.0/
Radixor modifications: Cleaning and packaging.
Revision status: not-recorded-in-legacy-import
The exact UniMorph commit used for the original Radixor import was not recorded.
Copyright (C) 2026, Leo Galambos.
Radixor-specific selection, verification, cleaning, normalization,
to the extent protected by applicable law.
The underlying morphological data remains attributed to UniMorph and
This derived model data, including Radixor's protectable contributions,
is distributed under Creative Commons Attribution-ShareAlike 3.0
Neither UniMorph nor any upstream contributor endorses Radixor.
'''
}
}

View File

@@ -1,4 +1,5 @@
plugins {
id 'org.egothor.radixor.build-support'
id 'java'
id 'eclipse'
id 'application'
@@ -7,9 +8,9 @@ plugins {
id 'pmd'
id 'jacoco'
id 'info.solidsoft.pitest' version '1.19.0'
id 'me.champeau.jmh' version '0.7.2'
id 'me.champeau.jmh' version '0.7.3'
id 'org.owasp.dependencycheck' version '12.2.1'
id 'org.cyclonedx.bom' version '3.2.4'
id 'org.cyclonedx.bom' version '3.3.0'
id 'com.palantir.git-version' version '4.0.0'
}
@@ -18,6 +19,8 @@ version = gitVersion(prefix:'release@')
def benchmarkReportsDirectory = layout.buildDirectory.dir('reports/jmh')
def sbomReportsDirectory = layout.buildDirectory.dir('reports/sbom')
def jmhIncludesProperty = providers.gradleProperty('jmh.includes')
.orElse(providers.systemProperty('jmh.includes'))
def nvdApiKey = providers.gradleProperty('nvdApiKey')
.orElse(providers.environmentVariable('NVD_API_KEY'))
@@ -28,6 +31,11 @@ apply from: 'gradle/maven-pom.gradle'
configurations {
mockitoAgent
stemmingQualityJmhRuntime {
canBeConsumed = false
canBeResolved = true
extendsFrom(jmhImplementation, jmhRuntimeOnly)
}
}
java {
@@ -38,6 +46,10 @@ java {
targetCompatibility = JavaVersion.VERSION_21
}
tasks.withType(JavaCompile).configureEach {
options.compilerArgs.addAll(['-Xlint:deprecation', '-Xlint:unchecked'])
}
tasks.withType(AbstractArchiveTask).configureEach {
preserveFileTimestamps = false
reproducibleFileOrder = true
@@ -63,6 +75,11 @@ dependencyLocking {
dependencies {
jmhImplementation sourceSets.main.output
modelProjects().each { Project modelProject ->
testRuntimeOnly project(modelProject.path)
jmhRuntimeOnly project(modelProject.path)
}
testImplementation platform(libs.junit.bom)
testImplementation libs.junit.jupiter
testRuntimeOnly libs.junit.platform.launcher
@@ -70,12 +87,55 @@ dependencies {
testImplementation libs.mockito.core
testImplementation libs.mockito.junit.jupiter
testImplementation libs.jqwik
testImplementation gradleTestKit()
mockitoAgent(libs.mockito.core) {
transitive = false
}
}
def modelProjects() {
Properties topology = new Properties()
rootProject.file('models/model-projects.properties').withInputStream { InputStream input ->
topology.load(input)
}
return topology.stringPropertyNames().toList().sort().collect { String modelId ->
project(":models:${modelId}")
}
}
def defaultModelProjects() {
Properties topology = new Properties()
rootProject.file('models/model-projects.properties').withInputStream { InputStream input ->
topology.load(input)
}
return topology.stringPropertyNames().findAll { String modelId ->
topology.getProperty(modelId) == 'default'
}.sort().collect { String modelId -> project(":models:${modelId}") }
}
tasks.named('projects') {
actions.clear()
doLast {
logger.lifecycle('Root project \'{}\'', rootProject.name)
rootProject.allprojects.findAll { Project candidate -> candidate != rootProject }
.sort { Project left, Project right -> left.path <=> right.path }
.each { Project candidate -> logger.lifecycle('+--- Project \'{}\'', candidate.path) }
gradle.includedBuilds.toList().sort { left, right -> left.name <=> right.name }
.each { includedBuild -> logger.lifecycle('Included build \'{}\'', includedBuild.name) }
}
}
sourceSets.jmh.compileClasspath = sourceSets.jmh.compileClasspath - sourceSets.test.output
sourceSets.jmh.runtimeClasspath = sourceSets.jmh.runtimeClasspath - sourceSets.test.output
sourceSets.test.compileClasspath += sourceSets.jmh.output + configurations.jmhCompileClasspath
sourceSets.test.runtimeClasspath += sourceSets.jmh.output + configurations.jmhCompileClasspath
tasks.named('compileJmhJava', JavaCompile) {
classpath = classpath - sourceSets.test.output
setDependsOn([tasks.named('classes')])
}
dependencyCheck {
failBuildOnCVSS = 7.0
failOnError = true
@@ -121,9 +181,10 @@ def splitTagExpression = { String tagsExpr ->
}
tasks.withType(Test).configureEach {
doFirst {
jvmArgs "-javaagent:${configurations.mockitoAgent.singleFile}"
}
final def mockitoAgentArguments = objects.newInstance(
org.egothor.radixor.MockitoAgentArgumentProvider)
mockitoAgentArguments.agentClasspath.from(configurations.mockitoAgent)
jvmArgumentProviders.add(mockitoAgentArguments)
/*
* Bundled dictionary integration tests compile and reload large real-world
@@ -139,6 +200,30 @@ tasks.withType(Test).configureEach {
}
}
tasks.named('test', Test) {
dependsOn('prepareModelConsumerTestRepository')
systemProperty('radixor.consumer.repository',
layout.buildDirectory.dir('model-consumer-repository').get().asFile.absolutePath)
systemProperty('radixor.core.version', version.toString())
systemProperty('radixor.catalog.version', project(':models:standard').version.toString())
}
tasks.register('modelDependencyResolutionTest', Test) {
group = 'verification'
description = 'Verifies that published model coordinates resolve from the generated consumer repository.'
dependsOn(tasks.named('prepareModelConsumerTestRepository'))
testClassesDirs = sourceSets.test.output.classesDirs
classpath = sourceSets.test.runtimeClasspath
useJUnitPlatform()
filter {
includeTestsMatching('org.egothor.stemmer.ModelDependencyResolutionTest')
}
systemProperty('radixor.consumer.repository',
layout.buildDirectory.dir('model-consumer-repository').get().asFile.absolutePath)
systemProperty('radixor.core.version', version.toString())
systemProperty('radixor.catalog.version', project(':models:standard').version.toString())
}
def configureJUnitPlatformTags = { Test task, String includeTagsExpr, String excludeTagsExpr ->
task.useJUnitPlatform {
final def includes = splitTagExpression(includeTagsExpr)
@@ -154,10 +239,49 @@ def configureJUnitPlatformTags = { Test task, String includeTagsExpr, String exc
}
tasks.named('test', Test) {
configureJUnitPlatformTags(it, cliIncludeTags, cliExcludeTags)
final def requestedIncludes = splitTagExpression(cliIncludeTags)
final boolean slowExplicitlyIncluded = requestedIncludes.contains('slow')
final String defaultExcludeTags = cliExcludeTags ?: (slowExplicitlyIncluded ? 'large-model' : 'slow,large-model')
configureJUnitPlatformTags(it, cliIncludeTags, defaultExcludeTags)
finalizedBy(tasks.named('jacocoTestReport'))
}
def largeModelMaxHeap = providers.gradleProperty('radixorLargeModelMaxHeap').orElse('6g')
def runtimeModelId = providers.gradleProperty('modelId').orElse('pl-pl-polimorf')
def runtimeModelClasspath = configurations.testRuntimeClasspath.incoming.artifactView {
componentFilter { componentIdentifier ->
if (!(componentIdentifier instanceof org.gradle.api.artifacts.component.ProjectComponentIdentifier)) {
return true
}
final String projectPath = componentIdentifier.projectPath
return !projectPath.startsWith(':models:') || projectPath == ":models:${runtimeModelId.get()}"
}
}.files
tasks.register('runtimeModelIntegrationTest', Test) {
group = 'verification'
description = 'Constructs one complete selected runtime model in an isolated, memory-sized JVM.'
testClassesDirs = sourceSets.test.output.classesDirs
classpath = sourceSets.test.output + sourceSets.main.output + sourceSets.jmh.output + runtimeModelClasspath
dependsOn(tasks.named('compileTestJava'))
useJUnitPlatform {
includeTags('large-model')
}
systemProperty('radixor.test.modelId', runtimeModelId.get())
minHeapSize = '1g'
maxHeapSize = largeModelMaxHeap.get()
maxParallelForks = 1
forkEvery = 1
reports {
junitXml.required = true
html.required = true
}
doFirst {
logger.lifecycle("Runtime model integration uses model '{}' with maximum heap {}.",
systemProperties.get('radixor.test.modelId'), maxHeapSize)
}
}
def configureTaggedTestProfile = { String taskName, String includeTagsExpr, String excludeTagsExpr = null,
String taskDescription = null, String testNameExcludePatterns = null ->
tasks.register(taskName, Test) {
@@ -169,10 +293,6 @@ def configureTaggedTestProfile = { String taskName, String includeTagsExpr, Stri
classpath = sourceSets.test.runtimeClasspath
dependsOn(tasks.named('compileTestJava'))
doFirst {
jvmArgs "-javaagent:${configurations.mockitoAgent.singleFile}"
}
if (testNameExcludePatterns != null && !testNameExcludePatterns.isBlank()) {
filter {
testNameExcludePatterns.split(',').each { String pattern ->
@@ -233,11 +353,19 @@ configureTaggedTestProfile(
configureTaggedTestProfile(
'ciRelease',
null,
'slow',
'slow,large-model',
'Release-profile validation of all non-slow tests.',
'org.egothor.stemmer.CompileIntegrationTest*,org.egothor.stemmer.StemmerPatchTrieLoaderTest$BundledDictionaryTests*'
)
tasks.named('ciRelease', Test) {
dependsOn('prepareModelConsumerTestRepository')
systemProperty('radixor.consumer.repository',
layout.buildDirectory.dir('model-consumer-repository').get().asFile.absolutePath)
systemProperty('radixor.core.version', version.toString())
systemProperty('radixor.catalog.version', project(':models:standard').version.toString())
}
configureTaggedTestProfile(
'ciNightly',
'fuzz',
@@ -313,25 +441,393 @@ tasks.named('check') {
// no-default, only on-demand: dependsOn(tasks.named('dependencyCheckAnalyze'))
}
allprojects {
tasks.matching { it.name == 'cyclonedxDirectBom' }.configureEach {
tasks.register('verifyCoreJarExcludesModels') {
group = 'verification'
description = 'Verifies that the root Radixor JAR contains no language dictionary bytes.'
dependsOn(tasks.named('jar'))
doLast {
File archive = tasks.named('jar', Jar).get().archiveFile.get().asFile
List<String> dictionaries = []
new java.util.zip.ZipFile(archive).withCloseable { zip ->
zip.entries().each { entry -> if (entry.name.endsWith('/stemmer.gz')) dictionaries.add(entry.name) }
}
if (!dictionaries.isEmpty()) {
throw new GradleException('The org.egothor:radixor JAR must not contain model data: ' + dictionaries)
}
}
}
tasks.register('verifyJavaLicenseHeaders') {
group = 'verification'
description = 'Verifies deterministic license classification for every maintained Java source file.'
inputs.file(layout.projectDirectory.file('gradle/java-license-header.txt'))
inputs.files(fileTree('src/main/java') { include '**/*.java' })
inputs.files(fileTree('src/test/java') { include '**/*.java' })
inputs.files(fileTree('src/jmh/java') { include '**/*.java' })
outputs.file(layout.buildDirectory.file('reports/license/java-license-headers.txt'))
doLast {
String canonicalHeader = layout.projectDirectory.file('gradle/java-license-header.txt')
.asFile.getText('UTF-8')
File canonicalSource = file('src/main/java/org/egothor/stemmer/CaseProcessingMode.java')
if (!canonicalSource.getText('UTF-8').startsWith(canonicalHeader)) {
throw new GradleException('CaseProcessingMode.java does not begin with the canonical Radixor license template.')
}
List<File> maintainedSources = files(
fileTree('src/main/java') { include '**/*.java' },
fileTree('src/test/java') { include '**/*.java' },
fileTree('src/jmh/java') { include '**/*.java' })
.files.toList().sort { File left, File right ->
relativePath(left) <=> relativePath(right)
}
List<String> classifications = []
List<String> failures = []
maintainedSources.each { File sourceFile ->
String relative = relativePath(sourceFile)
String content = sourceFile.getText('UTF-8')
if (content.startsWith(canonicalHeader)) {
classifications.add("RADIXOR_CANONICAL_HEADER ${relative}")
return
}
String leadingNotice = ''
if (content.startsWith('/*')) {
int closingIndex = content.indexOf('*/')
if (closingIndex >= 0) {
leadingNotice = content.substring(0, closingIndex + 2)
}
} else if (content.startsWith('//')) {
leadingNotice = content.readLines().takeWhile { String line ->
line.startsWith('//') || line.isBlank()
}.join('\n')
}
String remainder = content.substring(leadingNotice.length()).stripLeading()
boolean duplicateNotice = !leadingNotice.isEmpty()
&& (remainder.startsWith('/*') || remainder.startsWith('//'))
boolean historicalRadixor = leadingNotice =~ /(?s)Copyright \(C\) \d{4}(?:-\d{4})?, Leo Galambos/
&& leadingNotice.contains('All rights reserved.')
&& leadingNotice.contains('Redistribution and use in source and binary forms')
&& leadingNotice.contains('THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS')
boolean thirdPartyOrProvenance = leadingNotice =~ /(?is)(SPDX-License-Identifier|Licensed under|MIT License|Apache License|Permission is hereby granted|Original source|Adapted from|Ported from|Source:\s*\S)/
if (duplicateNotice) {
classifications.add("AMBIGUOUS_AUTHORSHIP ${relative}")
failures.add("${relative}: duplicate leading comment blocks")
} else if (historicalRadixor) {
classifications.add("RADIXOR_HISTORICAL_HEADER ${relative}")
} else if (thirdPartyOrProvenance) {
classifications.add("THIRD_PARTY_OR_PROVENANCE_HEADER ${relative}")
} else {
classifications.add("AMBIGUOUS_AUTHORSHIP ${relative}")
failures.add("${relative}: no recognized governing license or provenance header")
}
}
File report = layout.buildDirectory.file('reports/license/java-license-headers.txt').get().asFile
report.parentFile.mkdirs()
report.setText(classifications.join('\n') + '\n', 'UTF-8')
if (!failures.isEmpty()) {
throw new GradleException('Maintained Java license verification failed: '
+ failures.sort().join(', '))
}
}
}
tasks.register('verifyAllDefaultModels') {
group = 'verification'
description = 'Verifies that every language default model project is configured.'
dependsOn(defaultModelProjects().collect { Project modelProject ->
modelProject.path + ':verifyModelDescriptor'
})
}
tasks.register('verifyAllModels') {
group = 'verification'
description = 'Runs complete validation and artifact verification for every independently versioned model module.'
dependsOn(modelProjects().collect { Project modelProject -> modelProject.tasks.named('check') })
}
tasks.register('verifyJmhModelClasspath') {
group = 'verification'
description = 'Verifies that JMH receives each individual model JAR exactly once and embeds no dictionary.'
dependsOn(tasks.named('jmhJar'))
dependsOn(modelProjects().collect { Project modelProject -> modelProject.tasks.named('jar') })
outputs.file(layout.buildDirectory.file('reports/models/jmh-model-classpath.txt'))
doLast {
List<File> modelJars = configurations.jmhRuntimeClasspath.files.findAll { File dependency ->
dependency.name.startsWith('radixor-model-') && dependency.name.endsWith('.jar')
}.sort { File left, File right -> left.name <=> right.name }
List<String> expectedPrefixes = modelProjects().collect { Project modelProject ->
"radixor-model-${modelProject.name}-"
}
expectedPrefixes.each { String prefix ->
List<File> matches = modelJars.findAll { File dependency -> dependency.name.startsWith(prefix) }
if (matches.size() != 1) {
throw new GradleException("JMH must resolve exactly one model JAR with prefix ${prefix}; resolved ${matches}.")
}
}
if (modelJars.any { File dependency -> dependency.name.contains('benchmark-pack') }) {
throw new GradleException('JMH must not resolve a benchmark-pack artifact.')
}
File executable = tasks.named('jmhJar', Jar).get().archiveFile.get().asFile
if (!zipTree(executable).matching { include '**/stemmer.gz' }.isEmpty()) {
throw new GradleException('The JMH executable JAR must not embed model dictionaries.')
}
File report = layout.buildDirectory.file('reports/models/jmh-model-classpath.txt').get().asFile
report.parentFile.mkdirs()
report.setText(modelJars.collect { File dependency -> dependency.name }.join('\n') + '\n', 'UTF-8')
}
}
tasks.named('prepareModelConsumerTestRepository') {
coreVersion = version.toString()
catalogVersion = project(':models:standard').version.toString()
modelVersions = modelProjects().collectEntries { Project modelProject ->
final String modelVersion = providers.gradleProperty('modelReleaseVersion')
.orElse(providers.fileContents(modelProject.layout.projectDirectory.file('model-version.txt'))
.asText.map(String::trim))
.get()
[(modelProject.name): modelVersion]
}
corePom = layout.file(tasks.named('generatePomFileForMavenJavaPublication').map { it.destination })
coreJar = tasks.named('jar', Jar).flatMap { it.archiveFile }
modelPoms.from(modelProjects().collect { Project modelProject ->
modelProject.tasks.named('generatePomFileForModelPublication').map { it.destination }
})
modelJars.from(modelProjects().collect { Project modelProject ->
modelProject.tasks.named('jar', Jar).flatMap { it.archiveFile }
})
standardPom = layout.file(project(':models:standard').tasks.named('generatePomFileForStandardPublication')
.map { it.destination })
bomPom = layout.file(project(':models:bom').tasks.named('generatePomFileForBomPublication')
.map { it.destination })
repositoryDirectory = layout.buildDirectory.dir('model-consumer-repository')
}
def cleanModelCatalogStaging = tasks.register('cleanModelCatalogStaging') {
group = 'publishing'
description = 'Cleans the isolated model catalog Maven staging repository.'
doLast {
project.delete(layout.buildDirectory.dir('model-catalog-staging-repository'))
}
}
gradle.projectsEvaluated {
project(':models:standard').tasks.named('publishStandardPublicationToCatalogStagingRepository') {
dependsOn(cleanModelCatalogStaging)
}
project(':models:bom').tasks.named('publishBomPublicationToCatalogStagingRepository') {
dependsOn(cleanModelCatalogStaging)
}
}
tasks.register('prepareModelCatalogReleaseCandidate') {
group = 'publishing'
description = 'Stages the POM-only standard aggregate and model BOM with Central checksums.'
dependsOn(project(':models:standard').tasks.named('check'))
dependsOn(project(':models:bom').tasks.named('check'))
dependsOn(':models:standard:publishStandardPublicationToCatalogStagingRepository')
dependsOn(':models:bom:publishBomPublicationToCatalogStagingRepository')
outputs.dir(layout.buildDirectory.dir('model-catalog-staging-repository'))
doLast {
File repository = layout.buildDirectory.dir('model-catalog-staging-repository').get().asFile
repository.eachFileRecurse { File artifact ->
if (artifact.isFile() && !['.md5', '.sha1', '.sha256', '.sha512'].any {
String extension -> artifact.name.endsWith(extension)
}) {
['MD5': 'md5', 'SHA-1': 'sha1'].each { String algorithm, String extension ->
String digest = java.security.MessageDigest.getInstance(algorithm)
.digest(artifact.bytes).encodeHex().toString()
new File(artifact.absolutePath + ".${extension}").setText(digest, 'US-ASCII')
}
}
}
}
}
tasks.register('modelCatalogCentralBundle', Zip) {
group = 'publishing'
description = 'Builds the local POM-only model catalog bundle without remote publication.'
dependsOn(tasks.named('prepareModelCatalogReleaseCandidate'))
from(layout.buildDirectory.dir('model-catalog-staging-repository')) {
exclude('**/maven-metadata*.xml*', '**/*.module*')
}
destinationDirectory = layout.buildDirectory.dir('model-catalog-release-candidate')
archiveFileName = "radixor-models-catalog-${project(':models:standard').version}-central-bundle.zip"
doFirst {
if (providers.environmentVariable('GITHUB_REF_TYPE').orNull == 'tag'
&& (providers.environmentVariable('SIGNING_KEY').orNull?.isBlank() != false
|| providers.environmentVariable('SIGNING_PASSWORD').orNull?.isBlank() != false)) {
throw new GradleException('A tagged model catalog release requires SIGNING_KEY and SIGNING_PASSWORD.')
}
}
}
tasks.register('verifyModelCatalogReleaseCandidate') {
group = 'verification'
description = 'Verifies that the local catalog bundle contains only two POM publications, signatures when configured, and checksums.'
dependsOn(tasks.named('modelCatalogCentralBundle'))
outputs.file(layout.buildDirectory.file('reports/models/catalog-release-candidate.txt'))
doLast {
File bundle = tasks.named('modelCatalogCentralBundle', Zip).get().archiveFile.get().asFile
List<String> entries = []
new java.util.zip.ZipFile(bundle).withCloseable { java.util.zip.ZipFile archive ->
archive.entries().each { java.util.zip.ZipEntry entry ->
if (!entry.directory) entries.add(entry.name)
}
}
entries.sort()
List<String> poms = entries.findAll { String entry -> entry.endsWith('.pom') }
if (poms.size() != 2 || entries.any { String entry ->
entry.endsWith('.jar') || entry.endsWith('/stemmer.gz') || entry.contains('benchmark-pack')
}) {
throw new GradleException('The model catalog bundle must contain only the standard and BOM POM publications.')
}
List<String> unsupported = entries.findAll { String entry ->
!(entry.endsWith('.pom') || entry.endsWith('.pom.md5') || entry.endsWith('.pom.sha1')
|| entry.endsWith('.pom.sha256') || entry.endsWith('.pom.sha512')
|| entry.endsWith('.pom.asc') || entry.endsWith('.pom.asc.md5')
|| entry.endsWith('.pom.asc.sha1') || entry.endsWith('.pom.asc.sha256')
|| entry.endsWith('.pom.asc.sha512'))
}
if (!unsupported.isEmpty()) {
throw new GradleException("The model catalog bundle contains unsupported files: ${unsupported}.")
}
poms.each { String pom ->
if (!entries.contains(pom + '.md5') || !entries.contains(pom + '.sha1')) {
throw new GradleException("The model catalog POM is missing Central checksums: ${pom}.")
}
}
File report = layout.buildDirectory.file('reports/models/catalog-release-candidate.txt').get().asFile
report.parentFile.mkdirs()
report.setText("Bundle: ${bundle.name}\nBytes: ${bundle.length()}\n" + entries.join('\n') + '\n', 'UTF-8')
}
}
tasks.register('verifyArtifactSizes') {
group = 'verification'
description = 'Reports artifact sizes and rejects dictionary bytes in core.'
dependsOn(tasks.named('verifyCoreJarExcludesModels'))
doLast {
File archive = tasks.named('jar', Jar).get().archiveFile.get().asFile
println('org.egothor:radixor:' + version + ' ' + archive.length() + ' bytes')
}
}
tasks.named('check') {
dependsOn(tasks.named('verifyCoreJarExcludesModels'))
dependsOn(tasks.named('verifyAllDefaultModels'))
dependsOn(tasks.named('verifyJmhModelClasspath'))
dependsOn(tasks.named('verifyJavaLicenseHeaders'))
}
def modelCatalogText = providers.provider {
StringBuilder output = new StringBuilder()
output.append('| Model ID | Language | Default | Coordinates | Version | Source | Repository | Source version | Revision | Revision status | License | Attribution | SHA-256 | Bytes |\n')
output.append('|---|---|---:|---|---:|---|---|---|---|---|---|---|---|---:|\n')
modelProjects().each { Project modelProject ->
String script = modelProject.file('build.gradle').getText('UTF-8')
def value = { String key ->
def matcher = script =~ /(?m)^\s*${key}\s*=\s*(?:'([^']+)'|([^\s]+))\s*$/
if (!matcher.find()) return 'unavailable'
return matcher.group(1) != null ? matcher.group(1) : matcher.group(2)
}
File input = modelProject.file('src/modelInput/stemmer.gz')
String checksum = java.security.MessageDigest.getInstance('SHA-256').digest(input.bytes).encodeHex().toString()
output.append('| ').append(modelProject.name)
.append(' | ').append(value('language'))
.append(' | ').append(value('defaultModel'))
.append(' | org.egothor:radixor-model-').append(modelProject.name)
.append(' | ').append(modelProject.file('model-version.txt').text.trim())
.append(' | ').append(value('sourceName'))
.append(' | ').append(value('sourceRepository'))
.append(' | ').append(value('sourceVersion'))
.append(' | ').append(value('sourceRevision'))
.append(' | ').append(value('sourceRevisionStatus'))
.append(' | ').append(value('sourceLicense'))
.append(' | ').append(value('sourceAttribution'))
.append(' | ').append(checksum)
.append(' | ').append(input.length()).append(' |\n')
}
return output.toString()
}
tasks.register('generateModelCatalogDocumentation') {
group = 'documentation'
description = 'Generates the deterministic model catalog in the build documentation staging tree.'
outputs.file(layout.buildDirectory.file('mkdocs-source/stemmer-model-catalog.md'))
doLast {
File catalog = layout.buildDirectory.file('mkdocs-source/stemmer-model-catalog.md').get().asFile
catalog.parentFile.mkdirs()
catalog.setText('# Published Stemmer Model Catalog\n\n' + modelCatalogText.get(), 'UTF-8')
}
}
tasks.register('prepareMkDocsSource', Sync) {
group = 'documentation'
description = 'Stages maintained documentation, generated catalog, and MkDocs configuration under build/.'
dependsOn(modelProjects().collect { Project modelProject -> modelProject.path + ':verifyModelDescriptor' })
into(layout.buildDirectory.dir('mkdocs-source'))
from(layout.projectDirectory.dir('docs'))
doLast {
File catalog = layout.buildDirectory.file('mkdocs-source/stemmer-model-catalog.md').get().asFile
catalog.setText('# Published Stemmer Model Catalog\n\n' + modelCatalogText.get(), 'UTF-8')
File buildsPage = layout.buildDirectory.file('mkdocs-source/builds.md').get().asFile
buildsPage.setText('# Historical Builds\n\nThe Pages publication workflow replaces this staging placeholder with the retained build index.\n', 'UTF-8')
File configuration = layout.buildDirectory.file('mkdocs/mkdocs.yml').get().asFile
configuration.parentFile.mkdirs()
configuration.setText(layout.projectDirectory.file('mkdocs.yml').asFile.getText('UTF-8')
+ '\ndocs_dir: ../mkdocs-source\nsite_dir: ../mkdocs-site\n', 'UTF-8')
}
}
tasks.register('verifyModelCatalogDocumentation') {
group = 'verification'
description = 'Validates model metadata and the generated build-directory MkDocs catalog.'
dependsOn(tasks.named('prepareMkDocsSource'))
dependsOn(tasks.named('verifyAllDefaultModels'))
doLast {
File catalog = layout.buildDirectory.file('mkdocs-source/stemmer-model-catalog.md').get().asFile
String expected = '# Published Stemmer Model Catalog\n\n' + modelCatalogText.get()
if (!catalog.isFile() || catalog.getText('UTF-8') != expected) {
throw new GradleException('The staged model catalog is missing or nondeterministic.')
}
List<String> identifiers = modelProjects().collect { Project modelProject -> modelProject.name }
if (identifiers != identifiers.sort()) {
throw new GradleException('Published model projects are not in deterministic model-ID order.')
}
identifiers.each { String identifier ->
if (!expected.contains('org.egothor:radixor-model-' + identifier)) {
throw new GradleException('The staged model catalog omits published model ' + identifier + '.')
}
}
if (!layout.buildDirectory.file('mkdocs/mkdocs.yml').get().asFile.isFile()) {
throw new GradleException('The staged MkDocs configuration is missing.')
}
}
}
tasks.named('check') {
dependsOn(tasks.named('verifyModelCatalogDocumentation'))
}
tasks.named('cyclonedxDirectBom') {
includeConfigs = ['runtimeClasspath', 'compileClasspath']
skipConfigs = ['testRuntimeClasspath', 'testCompileClasspath', 'jmh.*', 'mockitoAgent']
includeBomSerialNumber = true
includeLicenseText = false
includeMetadataResolution = true
includeBuildSystem = true
}
}
tasks.named('cyclonedxBom') {
includeBomSerialNumber = true
includeLicenseText = false
includeBuildSystem = true
jsonOutput.set(sbomReportsDirectory.map { it.file('radixor-sbom.json') })
xmlOutput.set(sbomReportsDirectory.map { it.file('radixor-sbom.xml') })
}
subprojects {
tasks.matching { Task candidate -> candidate.name == 'cyclonedxDirectBom' }.configureEach {
enabled = false
description = 'Disabled because the root project exclusively owns CycloneDX SBOM generation.'
}
}
pitest {
pitestVersion = '1.22.1'
junit5PluginVersion = '1.2.3'
@@ -354,7 +850,10 @@ pitest {
excludedTestClasses = [
'org.egothor.stemmer.CompileIntegrationTest',
'org.egothor.stemmer.StemmerPatchTrieLoaderTest',
'org.egothor.stemmer.StemmerKnowledgeExperimentTest'
'org.egothor.stemmer.StemmerKnowledgeExperimentTest',
// These integration tests require dedicated Gradle task wiring and must not run in PIT worker JVMs.
'org.egothor.stemmer.FullRuntimeModelIntegrationTest',
'org.egothor.stemmer.ModelDependencyResolutionTest'
]
outputFormats = ['XML', 'HTML']
timestampedReports = false
@@ -421,6 +920,7 @@ tasks.named('distTar') {
jmh {
jmhVersion = '1.37'
includeTests = false
warmupIterations = 3
iterations = 5
fork = 1
@@ -430,13 +930,25 @@ jmh {
resultsFile = benchmarkReportsDirectory.map { it.file('jmh-results.csv').asFile }.get()
humanOutputFile = benchmarkReportsDirectory.map { it.file('jmh-results.txt').asFile }.get()
duplicateClassesStrategy = DuplicatesStrategy.EXCLUDE
if (jmhIncludesProperty.isPresent()) {
includes = [jmhIncludesProperty.get()]
}
}
tasks.named('jmh') {
group = 'verification'
description = 'Runs JMH benchmarks for the Radixor algorithmic core and Snowball comparison suite.'
description = 'Runs JMH benchmarks for the Radixor algorithmic core and external stemmer comparison suites.'
}
tasks.named('jmhJar', Jar) {
exclude 'META-INF/radixor/models.index'
exclude 'META-INF/radixor/models/**'
exclude 'org/egothor/stemmer/models/**'
exclude 'META-INF/LICENSES/**'
}
apply from: 'gradle/lucene-benchmarks.gradle'
tasks.register('regressionArtifactGenerator', JavaExec) {
group = 'verification'
description = 'Generates deterministic compiled trie regression artifacts.'
@@ -458,6 +970,100 @@ tasks.register('regressionArtifactGenerator', JavaExec) {
}
}
tasks.register('stemmingQuality', JavaExec) {
group = 'verification'
description = 'Evaluates pairwise over-stemming and under-stemming against registered model dictionary groups.'
dependsOn(tasks.named('testClasses'))
dependsOn(tasks.named('jmhClasses'))
dependsOn(tasks.named('prepareBenchmarkModelInputs'))
classpath = files(sourceSets.test.runtimeClasspath, configurations.stemmingQualityJmhRuntime)
mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityApplication'
args layout.buildDirectory.dir('reports/stemming-quality').get().asFile.absolutePath,
layout.buildDirectory.dir('generated/benchmark-model-inputs').get().asFile.absolutePath,
providers.gradleProperty('stemmingQualityLanguage').getOrElse(''),
providers.gradleProperty('stemmingQualityStemmer').getOrElse(''),
providers.gradleProperty('stemmingQualityMode').getOrElse(''),
providers.gradleProperty('stemmingQualityOutputPolicy').getOrElse(''),
providers.gradleProperty('stemmingQualityRankMetric').getOrElse('PAIRWISE_F05'),
providers.gradleProperty('stemmingQualityAudit').getOrElse('false'),
providers.gradleProperty('stemmingQualityAuditLimit').getOrElse('25')
maxHeapSize = '6g'
}
tasks.register('prepareBenchmarkModelInputs', Sync) {
group = 'verification'
description = 'Prepares default model inputs for JMH and quality evaluation without changing source data.'
into(layout.buildDirectory.dir('generated/benchmark-model-inputs'))
defaultModelProjects().each { Project modelProject ->
String languageDirectory = modelProject.name == 'pl-pl-unimorph'
? 'pl_pl'
: modelProject.name.replace('-default', '').replace('-', '_')
from(modelProject.file('src/modelInput/stemmer.gz')) {
into(languageDirectory)
}
}
}
tasks.register('publishStemmingQualityDocumentation', JavaExec) {
group = 'documentation'
description = 'Publishes validated complete stemming-quality results on the language benchmark pages.'
dependsOn(tasks.named('testClasses'))
classpath = sourceSets.test.runtimeClasspath
mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityDocumentationPublisher'
args layout.buildDirectory.file('reports/stemming-quality/stemming-quality.csv').get().asFile.absolutePath,
layout.projectDirectory.dir('docs').asFile.absolutePath,
'update'
doFirst {
if (!file("$buildDir/reports/stemming-quality/stemming-quality.csv").isFile()) {
throw new GradleException('A complete stemming-quality CSV is required. Run stemmingQuality only when no validated complete report is available.')
}
}
}
tasks.register('verifyStemmingQualityDocumentation', JavaExec) {
group = 'verification'
description = 'Verifies published language-page quality tables against the checked-in authoritative CSV.'
dependsOn(tasks.named('testClasses'))
classpath = sourceSets.test.runtimeClasspath
mainClass = 'org.egothor.stemmer.benchmark.quality.StemmingQualityDocumentationPublisher'
args layout.projectDirectory.file('docs/benchmarks/data/stemming-quality.csv').asFile.absolutePath,
layout.projectDirectory.dir('docs').asFile.absolutePath,
'verify'
}
tasks.named('check') {
dependsOn(tasks.named('verifyStemmingQualityDocumentation'))
}
tasks.register('verifyStemmingQualitySourceSets') {
group = 'verification'
description = 'Verifies the production, JMH, and standard-test ownership of stemming-quality infrastructure.'
doLast {
if (sourceSets.findByName('stemmingQualityTest') != null || file('src/stemmingQualityTest').exists()) {
throw new GradleException('The obsolete stemmingQualityTest source set or directory still exists.')
}
if (!file('src/jmh/java/org/egothor/stemmer/benchmark/QualityStemmerMatrix.java').isFile()) {
throw new GradleException('The authoritative JMH stemmer matrix is not in src/jmh.')
}
if (!file('src/test/java/org/egothor/stemmer/benchmark/quality/StemmingQualityApplication.java').isFile()) {
throw new GradleException('The stemming-quality evaluator is not in the standard test source set.')
}
}
}
tasks.register('verifyProductionJarExcludesStemmingQuality') {
group = 'verification'
description = 'Verifies that analytical stemming-quality classes are absent from the production JAR.'
dependsOn(tasks.named('jar'))
doLast {
final File archive = tasks.named('jar').get().archiveFile.get().asFile
final def forbidden = zipTree(archive).matching { include '**/benchmark/**' }.files
if (!forbidden.isEmpty()) {
throw new GradleException("Production JAR contains analytical stemming-quality classes: ${forbidden}")
}
}
}
tasks.register('printDependencyCheckNvdConfig') {
doLast {
System.out.println("NVD API key present: " + (nvdApiKey != null && !nvdApiKey.isBlank()))
@@ -506,6 +1112,10 @@ javadoc {
}
apply from: 'gradle/snowball-benchmarks.gradle'
apply from: 'gradle/paicehusk-benchmarks.gradle'
apply from: 'gradle/opennlp-benchmarks.gradle'
apply from: 'gradle/hunspell-benchmarks.gradle'
apply from: 'gradle/cistem-benchmarks.gradle'
gradle.taskGraph.whenReady { taskGraph ->
def banner = """

View File

@@ -17,6 +17,10 @@ The build-time flow is:
Dictionary -> Mutable trie -> Reduced trie -> Compiled trie
```
For registered models, the dictionary is an independently versioned GZip resource discovered through a descriptor and verified before this flow begins. The model resource is input to trie construction, not a precompiled trie. See [Model Selection and Loading](model-selection-and-loading.md) for discovery and [Architecture](architecture.md) for component and release boundaries.
Explicit descriptors and stable model IDs now use the same compiled-value path as language defaults. `loadCompiled(descriptor, ...)` and `loadCompiled(modelId, ...)` first build with serialized patch commands and then map those values to `CompiledPatchCommand` while preserving metadata, reduction semantics, and ranked `getAll` order. Very large inputs can have a high temporary construction peak; PoliMorf is verified in an isolated 6 GiB JVM rather than increasing ordinary test or Gradle daemon heaps.
At runtime, the compiled trie does not directly return the final stem string. It returns one or more stored patch commands for the addressed key, and those commands are then applied to the original input word.
## Why this matters
@@ -50,3 +54,5 @@ For most readers, the best order is:
- [Programmatic usage](programmatic-usage.md)
- [CLI compilation](cli-compilation.md)
- [Dictionary format](dictionary-format.md)
- [Model selection and loading](model-selection-and-loading.md)
- [Stemmer models](stemmer-models.md)

View File

@@ -2,6 +2,92 @@
This document explains the structural architecture of **Radixor**: what data is stored, how it flows through the build pipeline, and how runtime lookup works once a compiled trie has been produced.
## Component boundaries
| Component | Responsibility |
|---|---|
| Root Radixor core | Patch commands, dictionary parser, trie construction/lookup, descriptor and registry APIs, loaders; no language data |
| Individual model module | Immutable source input and license; publishes one independently versioned resource JAR |
| `StemmerModelRegistry` | Deterministic index/descriptor discovery and selection by model ID or language default |
| `StemmerModelDescriptor` | Immutable public view of validated runtime identity, format, resource, checksum, and source URL |
| Model convention plugin | Validates inputs and generates the resource namespace, descriptor, index, license, and publication |
| Standard aggregate | POM-only transitive runtime dependencies for one default per language |
| Verification classpaths | Direct individual-model dependencies for tests, quality evaluation, and JMH, including optional PoliMorf |
| Models BOM | POM-only recommended individual model versions in Maven dependency management |
| Documentation staging | Maintained `docs/` plus generated catalog under `build/mkdocs-source/` |
| Release workflows | Independent core, one-model, and catalog publication boundaries |
Read [Model Selection and Loading](model-selection-and-loading.md) for executable application examples and [Stemmer Models](stemmer-models.md) for artifact maintenance.
## Runtime model discovery and loading
The implemented sequence is:
1. use the thread context `ClassLoader`, or an explicit non-null loader;
2. enumerate every `META-INF/radixor/models.index` with `ClassLoader.getResources(...)`;
3. sort index URLs and validate every descriptor path;
4. read descriptor resources and required properties;
5. validate model ID, language, exact resource namespace, checksum syntax, format name, and format version;
6. sort descriptors by model ID and reject duplicate IDs;
7. resolve either `Language.defaultModelId()` or an exact explicit model ID;
8. open the declared model resource with the descriptor's discovering loader;
9. compare SHA-256 over the compressed bytes;
10. decompress GZip and parse UTF-8 Radixor dictionary rows;
11. build and reduce a `FrequencyTrie`;
12. optionally compile stored patch strings into `CompiledPatchCommand` values for the language-oriented compiled API.
Descriptor discovery verifies resource presence before selection. Byte-level checksum verification happens when the selected model is loaded. The registry never scans arbitrary JAR contents and never selects “the first model for a language.”
### Default Polish resolution
`StemmerPatchTrieLoader.Language.PL_PL` declares `pl-pl-unimorph` in the enum constructor. A language-oriented load creates a context-loader registry and calls `requireDefault(PL_PL)`. If that ID is absent, loading stops with `StemmerModelNotFoundException` naming `org.egothor:radixor-model-pl-pl-unimorph:<version>`.
### Explicit PoliMorf resolution
`registry.require("pl-pl-polimorf")` addresses the alternative directly. It neither changes nor consults the Polish default. Both descriptors may coexist; duplicate declarations of either same ID are rejected.
## Version axes
| Version | Owned by | Compatibility purpose |
|---|---|---|
| Core version | Root Git-derived release | Java implementation and public API |
| Model artifact version | Each `model-version.txt` | One independently published model JAR |
| Catalog version | `models/catalog-version.txt` | Standard aggregate and BOM recommendation set |
| Source dictionary version | Module provenance | Upstream lexical data lineage |
| Model format version | Descriptor and registry | Loader compatibility for packaged dictionary representation |
No equality relationship is implied between these values.
## Build topology and generated output
`models/model-projects.properties` is the single Gradle-readable topology list for the 21 individual model projects and their default or optional aggregate role. Per-model build scripts and generated descriptors remain authoritative for language, resource, provenance, checksum, and model-specific metadata. `settings.gradle`, root verification classpaths, the standard POM, and BOM constraints all derive membership from the topology list.
Gradle implicitly creates the lifecycle parent `:models` because child paths are nested. It has no build script, applied project plugin, Maven coordinate, publication, or archive. The root CycloneDX plugin exposes direct-task instances to subprojects internally; every subproject instance is disabled, so only root `:cyclonedxDirectBom` can generate an SBOM. The ignored path `models/build/` is generated output, not a module, and the supported build does not write reports there. Root aggregate reports, including `verifyJmhModelClasspath`, belong under `build/reports/models/`; each individual model retains its own outputs under `models/<model-id>/build/`.
`models/bom` is a Maven dependency BOM: it controls recommended dependency versions and adds no runtime artifacts. The root CycloneDX task produces a software bill of materials (SBOM) under `build/reports/sbom/`. These artifacts have different purposes and output locations.
## Build-time model packaging
The `org.egothor.radixor.model` convention plugin treats `src/modelInput` as immutable. `validateModelInput` checks the GZip stream, strict UTF-8, dictionary rows, ID, semantic model version, and license. `prepareModelResources` copies identical compressed bytes under `org/egothor/stemmer/models/<model-id>/stemmer.gz` and generates the descriptor, index, and packaged license under `build/`. `verifyModelDescriptor` checks the digest, while `verifyModelJar` checks the unique resource, packaged-byte digest, metadata, and dictionary-free documentation artifacts. The root `runtimeModelIntegrationTest` accepts `-PmodelId=<id>` and verifies transformation of a packaged resource into `FrequencyTrie<CompiledPatchCommand>`; PoliMorf release validation depends on this complete runtime test.
For UniMorph models, the convention validates and packages one model-specific attribution,
licensing, provenance, and contribution notice. Source and packaged notice bytes must match. The
notice identifies CC BY-SA 3.0 through its canonical URI; no project-wide CC license directory or
duplicated full legal text is used. Descriptors distinguish exact revisions from the explicit
legacy-import sentinel. UniMorph supplies morphological data; runtime patch commands and tries are
constructed by Radixor. The Java software remains BSD-3-Clause, while PoliMorf data remains under
its separately packaged BSD-2-Clause license.
## Release and security boundaries
| Tag | Publication boundary |
|---|---|
| `release@<core-version>` | Root `org.egothor:radixor` artifacts only; never model JARs |
| `model/<model-id>@<model-version>` | Exactly one matching model; never core, catalog, or other models |
| `models-catalog@<catalog-version>` | BOM and standard aggregate only; never model bytes |
License inclusion, strict metadata paths, resource presence, SHA-256 verification, unsupported-format rejection, and duplicate-ID rejection form the model integrity boundary. These checks detect packaging mistakes and corruption; model data remains non-executable dictionary input.
## The central idea
Radixor does not store final stems directly as a large flat lookup table. Instead, it stores **patch commands** that describe how a word form should be transformed into a canonical stem.
@@ -10,7 +96,7 @@ For example, if a dictionary states that `running` should reduce to `run`, the f
That matters because many words share similar transformation patterns. Once those mappings are organized in a trie and compiled into a canonical structure, the result is much smaller and more reusable than a naive direct-output table.
## End-to-end build flow
## Trie construction flow
The full build-time flow is:
@@ -53,6 +139,16 @@ Those patch-command values are inserted into a mutable trie keyed by the source
Equivalent subtrees are merged into canonical reduced nodes.
Before a selected semantic reduction mode is applied, Radixor also performs uniform-subtree
contraction. If every reachable entry below a subtree resolves to the same preferred patch
command, that subtree can be represented as an accepting leaf for that command. Runtime lookup can
then stop at that leaf even when the input word still has remaining characters.
This is a structural optimization of preferred-result lookup. It reduces trie depth in regions
where the remaining suffix cannot change the selected command, while preserving the `get()` result
used by the standard stemmer path. The benchmark tables in `docs/benchmarks/` are based on this
contracted compiled representation.
### Compilation
The reduced structure is frozen into an immutable compiled trie optimized for runtime lookup.
@@ -124,7 +220,7 @@ At runtime, lookup is conceptually simple:
3. retrieve one or more stored patch commands,
4. apply the chosen patch command to the original word.
The trie itself does not create the final stem string. It selects the stored transformation command. `PatchCommandEncoder.apply(...)` then performs the actual transformation.
The trie itself does not create the final stem string. It selects the stored transformation command. Runtime code should use `CompiledPatchCommand.apply(...)` so the serialized command is compiled once and reused.
That separation is architecturally important:
@@ -170,6 +266,7 @@ The final compiled trie can be much smaller than the original dictionary for sev
- patch commands are compact,
- trie paths reuse shared structure,
- uniform preferred-command subtrees can be contracted into accepting leaves,
- reduction merges equivalent subtrees,
- binary persistence stores the already reduced form,
- GZip compression is applied on top of the binary format.
@@ -180,7 +277,7 @@ This is why a very large dictionary can still produce a manageable deployable ru
The compactness of the final artifact should not be confused with the memory usage of preparation.
Before reduction has completed, the mutable build-time structure must exist in memory. For large dictionaries, that temporary preparation cost can be noticeably higher than the size of the final persisted artifact or the loaded compiled trie.
Before reduction has completed, the mutable build-time structure must exist in memory. For large dictionaries, that temporary preparation cost can be noticeably higher than the size of the final persisted artifact or the loaded compiled trie. PoliMorf is the exceptional current case: two complete test constructions took 23.7 and 23.5 seconds, produced 358,993 canonical nodes, and used a task-specific 6 GiB maximum heap. The process peak does not establish the retained heap of the final trie, which is not currently measured separately.
That is why the preferred operational model is usually:
@@ -207,3 +304,5 @@ Determinism matters not only for tests, but also for operational trust. It makes
- [Reduction Semantics](reduction-semantics.md)
- [Programmatic usage](programmatic-usage.md)
- [CLI compilation](cli-compilation.md)
- [Model selection and loading](model-selection-and-loading.md)
- [Stemmer models](stemmer-models.md)

View File

@@ -67,6 +67,104 @@
padding: 0.45rem 0.7rem;
}
/* Publication-quality benchmark tables retain identity columns while scrolling. */
.quality-table {
max-width: 100%;
overflow-x: auto;
margin: 0.65rem 0 1rem;
border: 1px solid var(--md-default-fg-color--lightest);
border-radius: 0.2rem;
scrollbar-gutter: stable;
}
.quality-table:focus {
outline: 0.15rem solid var(--md-accent-fg-color);
outline-offset: 0.1rem;
}
.quality-table::before {
content: "Scrollable table: Rank, Stemmer, and Output policy remain visible.";
display: block;
padding: 0.35rem 0.55rem;
color: var(--md-default-fg-color--light);
font-size: 0.68rem;
}
.quality-table .md-typeset__table,
.quality-table table {
margin: 0;
}
.quality-table table th:nth-child(1),
.quality-table table td:nth-child(1),
.quality-table table th:nth-child(2),
.quality-table table td:nth-child(2),
.quality-table table th:nth-child(3),
.quality-table table td:nth-child(3) {
position: sticky;
z-index: 2;
background: var(--md-default-bg-color);
background-clip: padding-box;
}
.quality-table table th:nth-child(1),
.quality-table table td:nth-child(1) {
left: 0;
min-width: 2.8rem;
}
.quality-table table th:nth-child(2),
.quality-table table td:nth-child(2) {
left: 2.8rem;
min-width: 13rem;
white-space: normal;
}
.quality-table table th:nth-child(3),
.quality-table table td:nth-child(3) {
left: 15.8rem;
min-width: 8.5rem;
box-shadow: 0.2rem 0 0.25rem rgb(0 0 0 / 8%);
}
.quality-details > summary {
font-weight: 600;
}
@media screen and (max-width: 44.99em) {
.quality-table table th:nth-child(2),
.quality-table table td:nth-child(2) {
min-width: 10rem;
}
.quality-table table th:nth-child(3),
.quality-table table td:nth-child(3) {
position: static;
min-width: 7.5rem;
box-shadow: none;
}
}
@media print {
.quality-table {
overflow: visible;
border: 0;
}
.quality-table::before {
display: none;
}
.quality-table table th,
.quality-table table td {
position: static !important;
}
.quality-details:not([open]) > *:not(summary) {
display: block;
}
}
/* Code blocks */
.md-typeset pre > code {
font-size: 0.72rem;

View File

@@ -1,159 +1,47 @@
# Benchmarking
Radixor includes a JMH benchmark suite for both the internal algorithmic core and a side-by-side English comparison against the Snowball Porter stemmer family.
Radixor contains internal trie microbenchmarks, a separate stemmer comparison suite, and a dictionary coverage benchmark for Radixor itself. Published stemmer comparison results must come only from benchmark classes matching `.*StemmerComparisonBenchmark.*`; internal `FrequencyTrie*` microbenchmarks are not part of those results.
This document explains what is benchmarked, how to run the suite, and how benchmark results should be interpreted.
Every current default Radixor benchmark scenario uses the model ID declared by its `Language.defaultModelId()`. The root JMH runtime configuration depends directly on all default model projects plus optional `pl-pl-polimorf`; no benchmark-pack project or artifact exists. These dependencies are benchmark-only and never enter the root published POM. A PoliMorf comparison must be labeled with model ID `pl-pl-polimorf`, while the default Polish row remains `pl-pl-unimorph`.
## Scope
The optional model now has a verified complete compiled loading path. This does not alter existing benchmark rows or make PoliMorf part of the representative English JMH run. Any future full PoliMorf benchmark must provision its documented startup heap independently and record the exact model artifact version and checksum.
The benchmark suite currently covers two categories:
This page is the entry point for benchmark interpretation. Detailed tables and long reference material are split into focused subpages so that important points do not get buried.
- Radixor core operations,
- English stemmer comparison on the same token workload.
## Key Takeaways
The comparison benchmark processes the same deterministic English token stream through:
- Speed and accuracy must be read together. A faster row is not necessarily a better stemmer.
- Radixor is the quality-oriented baseline in same-language comparisons. Its exact-root accuracy is often close to 100%, while many faster competitors are light, minimal, possessive, or aggressive rule-based stemmers with much lower root agreement.
- The measured Radixor cost buys dictionary-trained stemming precision. That precision improves search quality by mapping inflected forms to intended dictionary roots instead of approximate or over-reduced stems.
- Speed benchmarks process changed dictionary tokens where the surface form differs from the expected root. Accuracy benchmarks process the complete dictionary.
- Accuracy tables use deterministic auxiliary counters from the current JMH reports. Repeated measurement samples duplicate the same exact-root accounting and are not interpreted as timing results.
- The historical Porter performance badge is retired. Benchmark reporting now uses speed and quality tables rather than a single Porter ratio.
- Radixor with bundled `US_UK` (older benchmark snapshots used the now-retired `US_UK_PROFI` resource),
- Snowball original Porter,
- Snowball English, commonly referred to as Porter2.
## Benchmark Documentation Map
The purpose of the comparison is throughput measurement on identical input. It is not intended to demonstrate linguistic equivalence between the compared stemmers.
| Page | Purpose |
| --- | --- |
| [Benchmark methodology](benchmarks/reference/methodology.md) | Workload design, speed pass, quality pass, normalization policy, and exact-root metrics. |
| [Linguistic quality methodology](benchmarks/reference/linguistic-quality.md) | Pairwise gold standard, over/under-stemming, candidate policies, metrics, and ranking rules. |
| [Tested stemmers](benchmarks/reference/tested-stemmers.md) | Upstream attribution, tested versions, language coverage, adapter behaviour, and limitations. |
| [Reproducibility and raw data](benchmarks/reference/reproducibility.md) | Versioned quality snapshot, checksum, commands, reports, and provenance limitations. |
| [Benchmark corpora](benchmarks/reference/corpora.md) | Dictionary row counts, complete quality tokens, already-root tokens, changed speed tokens, and timing token counts. |
| [Benchmark environment and reports](benchmarks/reference/environment.md) | Hardware, OS, JVM, JMH settings, report files, and current badge/report policy. |
| [English dictionary coverage benchmark](benchmarks/reference/english-coverage.md) | The quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |
| [Candidate evaluation](benchmarks/reference/candidates.md) | Included benchmark families and evaluated candidates that were skipped. |
| [Language benchmark pages](benchmarks/languages/index.md) | Per-language accuracy tables, speed tables, and implementation notes. |
## How to read the published numbers
## How To Read Results
Two kinds of benchmark numbers are relevant in the project.
Start with the [language benchmark pages](benchmarks/languages/index.md). Each language page lists accuracy first and speed second because throughput without root agreement is not enough to interpret stemmer quality.
### Reference measurements
When Radixor is slower than a narrow competitor, check the accuracy table before drawing a conclusion. Many Lucene light/minimal filters and possessive filters intentionally do less work. They can be fast precisely because they are not trying to match the dictionary root with the same precision.
The detailed benchmark snapshot documented on this page comes from a controlled run on a Ryzen 5 system. Those numbers are the best reference point for understanding absolute throughput under a known local benchmark environment.
The [English dictionary coverage benchmark](benchmarks/reference/english-coverage.md) shows the central operating curve explicitly: contracted tries preserve high quality even at reduced dictionary coverage, while changed-form exactness still reflects how much language knowledge was available during training. This is why Radixor performance should be discussed as a configurable quality/speed point, not as a single fixed ratio against Porter.
### Published badge figures
## Current Result Locations
The benchmark badge metadata published through GitHub Pages is generated in the GitHub-hosted container environment. That environment is convenient for continuous publication, but it is not the right place to treat absolute throughput values as stable across time. CPU scheduling, shared-host variability, and container-level noise can materially affect raw numbers from run to run.
The current measured language results are published in [Language Benchmark Pages](benchmarks/languages/index.md). Generated local report files for this benchmark update are listed in [Benchmark environment and reports](benchmarks/reference/environment.md).
For that reason, the published badge values should be treated primarily as a compact status surface. They are useful for observing broad trends and relative positioning, but not as the authoritative source for precise absolute throughput claims.
## Current snapshot
A recent JMH run on JDK 21.0.10 with JMH 1.37, one thread, three warmup iterations, and five measurement iterations produced the following approximate throughput ranges:
| Workload | Radixor `US_UK` *(historical runs: `US_UK_PROFI`)* | Snowball Porter | Snowball English |
| --- | ---: | ---: | ---: |
| About 12,000 generated tokens | 30.99 M tokens/s | 8.21 M tokens/s | 5.46 M tokens/s |
| About 60,000 generated tokens | 32.25 M tokens/s | 8.02 M tokens/s | 5.11 M tokens/s |
On that workload, Radixor measured approximately:
- 4 times the throughput of Snowball original Porter,
- 6 times the throughput of Snowball English.
These values are workload-dependent and environment-dependent. They should be read as measured results for the documented setup, not as universal constants.
## Interpreting the relative result
Although the absolute numbers can move across environments, the throughput relationship between Radixor and the compared Porter-family stemmers has remained broadly stable in practical measurements. In particular, the comparison against Snowball original Porter is consistently in the rough range of about four to one in Radixors favor.
That relative behavior is more informative than any single absolute figure. It reflects a real architectural difference rather than a cosmetic benchmark artifact.
Radixor is built around a compiled patch-command trie that resolves the result through a direct lookup and patch application path. In contrast, classic rule-based stemmers such as the Porter family follow a different operational model. The result is that Radixor combines two properties that do not often appear together:
- dictionary-driven compiled lookup performance,
- the ability to generalize beyond explicitly listed word forms instead of behaving like a pure closed-form dictionary lookup table.
Within that design space, the measured throughput profile is strong enough to place Radixor among the fastest known practical implementations of this kind, while still supporting stemming of previously unseen forms. That should still be read as a carefully bounded engineering statement, not as an absolute claim over every possible stemmer architecture or benchmark scenario.
## Benchmark classes
The main benchmark classes are under `src/jmh/java/org/egothor/stemmer/benchmark`.
Relevant classes include:
- `FrequencyTrieLookupBenchmark`,
- `FrequencyTrieCompilationBenchmark`,
- `EnglishStemmerComparisonBenchmark`.
The English comparison benchmark uses the bundled Radixor English resource and the official Snowball Java distribution integrated into the JMH source set.
## Workload design
The English comparison benchmark uses a deterministic generated corpus rather than an uncontrolled ad hoc text sample.
The workload intentionally mixes:
- simple inflections,
- common derivational forms,
- US and UK spelling families,
- lexical forms appropriate for the current bundled `US_UK` resource (with historical continuity from earlier `US_UK_PROFI` runs).
This design keeps runs reproducible across environments and avoids accidental drift caused by changing external corpora.
## Running benchmarks
Run the full benchmark suite:
```bash
./gradlew jmh
```
Run only the English comparison benchmark:
```bash
./gradlew jmh -Pjmh.includes=EnglishStemmerComparisonBenchmark
```
## Generated reports
JMH reports are written to:
- `build/reports/jmh/jmh-results.txt`,
- `build/reports/jmh/jmh-results.csv`.
The text report is convenient for human review. The CSV report is more useful for CI archiving, historical tracking, and external processing.
## Interpreting results responsibly
Benchmark numbers should always be read with care.
Important factors include:
- CPU model and frequency behavior,
- thermal throttling,
- JVM vendor and version,
- system background load,
- operating-system scheduling noise,
- benchmark parameter changes.
For meaningful comparison, keep these stable:
- hardware or VM class,
- JDK version,
- benchmark parameters,
- thread count,
- benchmark source revision.
If a regression is suspected, repeat the run and compare against previous CSV output rather than relying on a single measurement.
## Regression tracking
The recommended regression workflow is:
1. archive `jmh-results.csv`,
2. compare the same benchmark names across runs,
3. compare only like-for-like environments,
4. investigate sustained regressions rather than one-off noise.
For public reporting, the README should keep only the condensed benchmark summary, while detailed benchmark methodology and interpretation should remain in this document.
## Notes on comparison fairness
Radixor, Snowball Porter, and Snowball English are not the same kind of stemmer.
Radixor uses a compiled patch-command trie driven by dictionary data. Snowball Porter and Snowball English are rule-based English stemmers.
Because of that, the comparison should be understood as:
- equal input workload,
- different stemming strategies,
- measured throughput rather than semantic identity.
That distinction matters whenever performance claims are discussed in documentation, release notes, or badge summaries.
JMH TXT and CSV reports are still published as benchmark artifacts. They are no longer converted into a Shields endpoint benchmark badge.
Model IDs, independent artifact versions, and descriptor checksums identify inputs for future reproducibility. Historical snapshots remain tied to the model inputs used when measured; the optional PoliMorf model must not be retroactively attributed to results that predate it. See [Model Selection and Loading](model-selection-and-loading.md) and [Reproducibility](benchmarks/reference/reproducibility.md).

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Stemmer,Language,Dictionary mode,Output policy,Applied dictionary rows,Processed word forms,Singleton dictionary rows,Forms with one candidate,Forms with multiple candidates,Maximum candidates for one form,Total candidate assignments,Distinct output stems,True-positive pairs,False-positive pairs,False-negative pairs,True-negative pairs,Over-stemming error pairs,Over-stemming possible pairs,Over-stemming percentage,Under-stemming error pairs,Under-stemming possible pairs,Under-stemming percentage,Pairwise precision,Pairwise recall,Pairwise specificity,Pairwise accuracy,Balanced accuracy,Pairwise F0.5,Pairwise F1,Pairwise F2,Jaccard index,Fowlkes-Mallows index,Matthews correlation coefficient,Pairwise error rate,Adjusted Rand Index,Homogeneity,Completeness,V-measure,Normalized mutual information
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"UKRAINIAN_RADIXOR","UK_UA","ALL_WORDS","ALL_CANDIDATES","1493","14245","4","14055","190","2","14435","1493","65340","1490","0","101386060","1490","101387550","0.001470","0","65340","0.000000","0.977704623672","1.000000000000","0.999985303916","0.999985313380","0.999992651958","0.982083809295","0.988726639933","0.995459946982","0.977704623672","0.988789473888","0.988782208195","0.000014686620","","","","",""
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"YI_RADIXOR","YI","ALL_WORDS","PRIMARY_OUTPUT","802","3578","0","3578","0","1","3578","802","6195","195","149","6392714","195","6392909","0.003050","149","6344","2.348676","0.969483568075","0.976513240858","0.999969497454","0.999946243726","0.988241369156","0.970881394183","0.972985707555","0.975099162627","0.947392567671","0.972992055990","0.972965163911","0.000053756274","0.972958803000","0.995691103897","0.996142223728","0.995916612726","0.995916612726"
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1 Stemmer Language Dictionary mode Output policy Applied dictionary rows Processed word forms Singleton dictionary rows Forms with one candidate Forms with multiple candidates Maximum candidates for one form Total candidate assignments Distinct output stems True-positive pairs False-positive pairs False-negative pairs True-negative pairs Over-stemming error pairs Over-stemming possible pairs Over-stemming percentage Under-stemming error pairs Under-stemming possible pairs Under-stemming percentage Pairwise precision Pairwise recall Pairwise specificity Pairwise accuracy Balanced accuracy Pairwise F0.5 Pairwise F1 Pairwise F2 Jaccard index Fowlkes-Mallows index Matthews correlation coefficient Pairwise error rate Adjusted Rand Index Homogeneity Completeness V-measure Normalized mutual information
2 CZECH_LUCENE_CZECH_STEM_FILTER CS_CZ ALL_WORDS PRIMARY_OUTPUT 5113 51676 2 51676 0 1 51676 9647 177249 14480 124586 1334862335 14480 1334876815 0.001085 124586 301835 41.276194 0.924476735392 0.587238060530 0.999989152557 0.999895844650 0.793613606543 0.829234311828 0.718241200736 0.633453389361 0.560355974266 0.736809286788 0.736765291417 0.000104155350 0.718191706079 0.993800637348 0.944976928457 0.968774025802 0.968774025802
3 CZECH_LUCENE_CZECH_STEM_FILTER CS_CZ LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 5038 50968 2 50968 0 1 50968 9558 174387 13950 124426 1298530265 13950 1298544215 0.001074 124426 298813 41.640089 0.925930645598 0.583599107134 0.999989257201 0.999893462107 0.791794182167 0.828708724235 0.715947860002 0.630197985095 0.557569149804 0.735100195918 0.735055396892 0.000106537893 0.715897321649 0.993897397445 0.944297456221 0.968462775946 0.968462775946
4 CZECH_RADIXOR CS_CZ ALL_WORDS PRIMARY_OUTPUT 5113 51676 2 51676 0 1 51676 5162 299762 3867 2073 1334872948 3867 1334876815 0.000290 2073 301835 0.686799 0.987264062392 0.993132009210 0.999997103103 0.999995551157 0.996564556157 0.988432097845 0.990189342389 0.991952846154 0.980569312599 0.990193689085 0.990191466141 0.000004448843 0.990187117482 0.998733220675 0.998685552738 0.998709386137 0.998709386137
5 CZECH_RADIXOR CS_CZ ALL_WORDS ANY_CANDIDATE 5113 51676 2 51080 596 4 52319 5166 301835 0 0 1334876815 0 1334876815 0.000000 0 301835 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
6 CZECH_RADIXOR CS_CZ ALL_WORDS ALL_CANDIDATES 5113 51676 2 51080 596 4 52319 5166 301835 5850 0 1334870965 5850 1334876815 0.000438 0 301835 0.000000 0.980987048442 1.000000000000 0.999995617573 0.999995618564 0.999997808787 0.984731579205 0.990402283764 0.996138677580 0.980987048442 0.990447902942 0.990445732657 0.000004381436
7 CZECH_RADIXOR CS_CZ LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 5038 50968 2 50968 0 1 50968 5037 297104 3863 1709 1298540352 3863 1298544215 0.000297 1709 298813 0.571930 0.987164705765 0.994280703985 0.999997025130 0.999995710028 0.997138864558 0.988579745136 0.990709926973 0.992849308824 0.981590876052 0.990716315904 0.990714173387 0.000004289972 0.990707781520 0.998726091764 0.999029907266 0.998877976413 0.998877976413
8 CZECH_RADIXOR CS_CZ LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 5038 50968 2 50428 540 4 51543 5040 298813 0 0 1298544215 0 1298544215 0.000000 0 298813 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
9 CZECH_RADIXOR CS_CZ LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 5038 50968 2 50428 540 4 51543 5040 298813 5782 0 1298538433 5782 1298544215 0.000445 0 298813 0.000000 0.981017416570 1.000000000000 0.999995547321 0.999995548346 0.999997773661 0.984756059381 0.990417760454 0.996144940117 0.981017416570 0.990463233325 0.990461028216 0.000004451654
10 DA_DK_RADIXOR DA_DK ALL_WORDS PRIMARY_OUTPUT 4179 28079 32 28079 0 1 28079 4184 89188 1165 707 394110021 1165 394111186 0.000296 707 89895 0.786473 0.987106128186 0.992135268925 0.999997043981 0.999995251155 0.996066156453 0.988107873355 0.989614309174 0.991125345329 0.979442126071 0.989617503860 0.989615130363 0.000004748845 0.989611934224 0.998465862775 0.998718664384 0.998592247580 0.998592247580
11 DA_DK_RADIXOR DA_DK ALL_WORDS ANY_CANDIDATE 4179 28079 32 27756 323 3 28405 4187 89895 0 0 394111186 0 394111186 0.000000 0 89895 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
12 DA_DK_RADIXOR DA_DK ALL_WORDS ALL_CANDIDATES 4179 28079 32 27756 323 3 28405 4187 89895 1849 0 394109337 1849 394111186 0.000469 0 89895 0.000000 0.979846093478 1.000000000000 0.999995308431 0.999995309500 0.999997654215 0.983811622975 0.989820468071 0.995903164910 0.979846093478 0.989871756076 0.989869434047 0.000004690500
13 DA_DK_RADIXOR DA_DK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4173 28033 32 28033 0 1 28033 4170 89077 1165 663 392819623 1165 392820788 0.000297 663 89740 0.738801 0.987090268389 0.992611990194 0.999997034271 0.999995347541 0.996304512232 0.988189692661 0.989843428787 0.991502709249 0.979891095099 0.989847279032 0.989844954043 0.000004652459 0.989841102043 0.998463063294 0.998811876590 0.998637439483 0.998637439483
14 DA_DK_RADIXOR DA_DK LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 4173 28033 32 27718 315 3 28351 4173 89740 0 0 392820788 0 392820788 0.000000 0 89740 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
15 DA_DK_RADIXOR DA_DK LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 4173 28033 32 27718 315 3 28351 4173 89740 1849 0 392818939 1849 392820788 0.000471 0 89740 0.000000 0.979811986156 1.000000000000 0.999995293019 0.999995294094 0.999997646509 0.983784115625 0.989803065147 0.995896117847 0.979811986156 0.989854527774 0.989852198158 0.000004705906
16 ENGLISH_LUCENE_KSTEM_FILTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 371125 237565 1368501 76305 184489083270 1368501 184490451771 0.000742 76305 313870 24.311020 0.147917333410 0.756889795138 0.999992582267 0.999992168681 0.878441188702 0.176283968232 0.247471790726 0.415099040868 0.141208449266 0.334599940499 0.334597833111 0.000007831319 0.247469648794 0.980686838187 0.992107972963 0.986364345289 0.986364345289
17 ENGLISH_LUCENE_KSTEM_FILTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 347624 237551 1367069 74340 170473473135 1367069 170474840204 0.000802 74340 311891 23.835250 0.148041904002 0.761647498645 0.999991980817 0.999991544756 0.880819739731 0.176476898525 0.247899438093 0.416437018089 0.141486991947 0.335791223646 0.335788961354 0.000008455244 0.247897133948 0.979822223835 0.991990504792 0.985868818390 0.985868818390
18 ENGLISH_LUCENE_MINIMAL_FILTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 453328 137225 1122264 176645 184489329507 1122264 184490451771 0.000608 176645 313870 56.279670 0.108952916619 0.437203300730 0.999993916953 0.999992959490 0.718598608842 0.128203906480 0.174435713655 0.272816484020 0.095551668577 0.218253464509 0.218250987161 0.000007040510 0.174433464995 0.995202198233 0.981173943304 0.988138284715 0.988138284715
19 ENGLISH_LUCENE_MINIMAL_FILTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 430129 136932 1120871 174959 170473719333 1120871 170474840204 0.000657 174959 311891 56.096200 0.108866014789 0.439037997249 0.999993425006 0.999992398716 0.719515711128 0.128139023335 0.174469673707 0.273277328232 0.095572048952 0.218623688336 0.218621020778 0.000007601284 0.174467253215 0.994993790771 0.980519680106 0.987703711280 0.987703711280
20 ENGLISH_LUCENE_PORTER_COPIED US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 319968 285390 1557406 28480 184488894365 1557406 184490451771 0.000844 28480 313870 9.073820 0.154867928951 0.909261796285 0.999991558338 0.999991403982 0.954626677312 0.185678591198 0.264658505304 0.460562583837 0.152510906996 0.375253902399 0.375251973425 0.000008596018 0.264656367392 0.969648379409 0.997199419831 0.983230936080 0.983230936080
21 ENGLISH_LUCENE_PORTER_COPIED US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 298779 283761 1552702 28130 170473287502 1552702 170474840204 0.000911 28130 311891 9.019177 0.154514956196 0.909808234287 0.999990891899 0.999990726907 0.954899563093 0.185277176317 0.264165961476 0.460049474275 0.152183881415 0.374938634269 0.374936557303 0.000009273093 0.264163659878 0.968644600847 0.997108656044 0.982670549062 0.982670549062
22 ENGLISH_LUCENE_PORTER_FILTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 319968 285390 1557406 28480 184488894365 1557406 184490451771 0.000844 28480 313870 9.073820 0.154867928951 0.909261796285 0.999991558338 0.999991403982 0.954626677312 0.185678591198 0.264658505304 0.460562583837 0.152510906996 0.375253902399 0.375251973425 0.000008596018 0.264656367392 0.969648379409 0.997199419831 0.983230936080 0.983230936080
23 ENGLISH_LUCENE_PORTER_FILTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 298779 283761 1552702 28130 170473287502 1552702 170474840204 0.000911 28130 311891 9.019177 0.154514956196 0.909808234287 0.999990891899 0.999990726907 0.954899563093 0.185277176317 0.264165961476 0.460049474275 0.152183881415 0.374938634269 0.374936557303 0.000009273093 0.264163659878 0.968644600847 0.997108656044 0.982670549062 0.982670549062
24 ENGLISH_LUCENE_POSSESSIVE_FILTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 591899 7 1115154 313863 184489336617 1115154 184490451771 0.000604 313863 313870 99.997770 0.000006277121 0.000022302227 0.999993955492 0.999992254263 0.500008128860 0.000007330589 0.000009796848 0.000014763939 0.000004898448 0.000011831896 0.000008625150 0.000007745737 0.000007141644 0.995789196698 0.958018540631 0.976538780935 0.976538780935
25 ENGLISH_LUCENE_POSSESSIVE_FILTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 568400 5 1113773 311886 170473726431 1113773 170474840204 0.000653 311886 311891 99.998397 0.000004489225 0.000016031242 0.999993466643 0.999991637145 0.500004748942 0.000005244385 0.000007014251 0.000010587200 0.000003507138 0.000008483387 0.000005026087 0.000008362855 0.000004155674 0.995605378040 0.956423154691 0.975621022465 0.975621022465
26 ENGLISH_OPENNLP_PORTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 319968 285390 1557406 28480 184488894365 1557406 184490451771 0.000844 28480 313870 9.073820 0.154867928951 0.909261796285 0.999991558338 0.999991403982 0.954626677312 0.185678591198 0.264658505304 0.460562583837 0.152510906996 0.375253902399 0.375251973425 0.000008596018 0.264656367392 0.969648379409 0.997199419831 0.983230936080 0.983230936080
27 ENGLISH_OPENNLP_PORTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 298779 283761 1552702 28130 170473287502 1552702 170474840204 0.000911 28130 311891 9.019177 0.154514956196 0.909808234287 0.999990891899 0.999990726907 0.954899563093 0.185277176317 0.264165961476 0.460049474275 0.152183881415 0.374938634269 0.374936557303 0.000009273093 0.264163659878 0.968644600847 0.997108656044 0.982670549062 0.982670549062
28 ENGLISH_PAICE_HUSK_LANCASTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 268169 283991 3062661 29879 184487389110 3062661 184490451771 0.001660 29879 313870 9.519546 0.084858240415 0.904804536910 0.999983399352 0.999983237427 0.952393968131 0.103642734217 0.155164208820 0.308542866654 0.084107327905 0.277092260667 0.277089454298 0.000016762573 0.155161580693 0.937768073854 0.996599815184 0.966289292109 0.966289292109
29 ENGLISH_PAICE_HUSK_LANCASTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 249411 282398 3045870 29493 170471794334 3045870 170474840204 0.001787 29493 311891 9.456188 0.084848335531 0.905438117804 0.999982133023 0.999981960051 0.952710125414 0.103632575002 0.155156958803 0.308575577075 0.084103067491 0.277173081705 0.277170064389 0.000018039949 0.155154132315 0.936076835754 0.996486960554 0.965337716341 0.965337716341
30 ENGLISH_RADIXOR US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 390361 292001 1149886 21869 184489301885 1149886 184490451771 0.000623 21869 313870 6.967534 0.202513095686 0.930324656705 0.999993767233 0.999993648707 0.965159211969 0.240076409811 0.332621199859 0.541270431499 0.199487482886 0.434054059102 0.434052478080 0.000006351293 0.332619335001 0.994214506865 0.997769723414 0.995988942533 0.995988942533
31 ENGLISH_RADIXOR US_UK ALL_WORDS ANY_CANDIDATE 396939 607439 250964 578231 29208 1355 2838145 397392 313855 12 15 184490451759 12 184490451771 0.000000 15 313870 0.004779 0.999961767245 0.999952209513 0.999999999935 0.999999999854 0.999976104724 0.999959855684 0.999956988357 0.999954121045 0.999913980413 0.999956988368 0.999956988295 0.000000000146
32 ENGLISH_RADIXOR US_UK ALL_WORDS ALL_CANDIDATES 396939 607439 250964 578231 29208 1355 2838145 397392 313855 11482166 15 184478969605 11482166 184490451771 0.006224 15 313870 0.004779 0.026606853277 0.999952209513 0.999937762817 0.999937762842 0.999944986165 0.033038791524 0.051834488023 0.120237128281 0.026606819443 0.163112175274 0.163107098882 0.000062237158
33 ENGLISH_RADIXOR US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 367590 290572 1148489 21319 170473691715 1148489 170474840204 0.000674 21319 311891 6.835401 0.201917778329 0.931645991709 0.999993263000 0.999993137956 0.965819627354 0.239424468968 0.331901731173 0.540775136091 0.198970131062 0.433723286019 0.433721583515 0.000006862044 0.331899721995 0.993959181482 0.997731171071 0.995841604460 0.995841604460
34 ENGLISH_RADIXOR US_UK LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 374384 583910 228735 555084 28826 1355 2812871 374506 311891 0 0 170474840204 0 170474840204 0.000000 0 311891 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
35 ENGLISH_RADIXOR US_UK LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 374384 583910 228735 555084 28826 1355 2812871 374506 311891 11470018 0 170463370186 11470018 170474840204 0.006728 0 311891 0.000000 0.026472025883 1.000000000000 0.999932717239 0.999932717362 0.999966358619 0.032872482055 0.051578660140 0.119686728696 0.026472025883 0.162702261457 0.162696787836 0.000067282638
36 ENGLISH_SNOWBALL_ORIGINAL_PORTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 321092 285304 1555293 28566 184488896478 1555293 184490451771 0.000843 28566 313870 9.101220 0.155006228957 0.908987797496 0.999991569791 0.999991414969 0.954489683644 0.185835337999 0.264848800190 0.460750814660 0.152637303435 0.375364850057 0.375362921954 0.000008585031 0.264846663203 0.969891477221 0.997192899073 0.983352728141 0.983352728141
37 ENGLISH_SNOWBALL_ORIGINAL_PORTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 299877 283675 1550615 28216 170473289589 1550615 170474840204 0.000910 28216 311891 9.046750 0.154651118416 0.909532496930 0.999990904142 0.999990738644 0.954761700536 0.185431499934 0.264353286139 0.460234326480 0.152308234175 0.375046954242 0.375044878196 0.000009261356 0.264350985524 0.968893806180 0.997101844661 0.982795461434 0.982795461434
38 ENGLISH_SNOWBALL_PORTER2 US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 318385 285334 1566711 28536 184488885060 1566711 184490451771 0.000849 28536 313870 9.091662 0.154064291094 0.909083378469 0.999991507902 0.999991353242 0.954537443185 0.184752753479 0.263476636895 0.459101696688 0.151726514306 0.374242282819 0.374240346981 0.000008646758 0.263474493989 0.969037354042 0.997181597682 0.982908049045 0.982908049045
39 ENGLISH_SNOWBALL_PORTER2 US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 297220 283730 1561891 28161 170473278313 1561891 170474840204 0.000916 28161 311891 9.029116 0.153731454074 0.909708840589 0.999990837997 0.999990672823 0.954849839293 0.184374949232 0.263015918336 0.458637294569 0.151421029768 0.373966392672 0.373964308569 0.000009327177 0.263013611479 0.968019617024 0.997095706553 0.982342555079 0.982342555079
40 FINNISH_LUCENE_FINNISH_LIGHT_STEM_FILTER FI_FI ALL_WORDS PRIMARY_OUTPUT 57027 1811717 292 1811717 0 1 1811717 439975 12355389 2223150 19168306 1641124591341 2223150 1641126814491 0.000135 19168306 31523695 60.806025 0.847505295284 0.391939745642 0.999998645351 0.999986965635 0.695969195497 0.687649407375 0.535999578676 0.439151826652 0.366119825424 0.576342788507 0.576337821084 0.000013034365 0.535993941880 0.988126027331 0.886473473160 0.934543630400 0.934543630400
41 FINNISH_LUCENE_FINNISH_LIGHT_STEM_FILTER FI_FI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 54762 1757055 274 1757055 0 1 1757055 431848 11988389 1806392 18825444 1543587637760 1806392 1543589444152 0.000117 18825444 30813833 61.094133 0.869052506162 0.389058673746 0.999998829746 0.999986634125 0.694528751746 0.697056446146 0.537492108587 0.437372459518 0.367513988637 0.581474346349 0.581469391800 0.000013365875 0.537486398327 0.989268269625 0.885293761089 0.934397488899 0.934397488899
42 FINNISH_RADIXOR FI_FI ALL_WORDS PRIMARY_OUTPUT 57027 1811717 292 1811717 0 1 1811717 69091 30552427 731279 971268 1641126083212 731279 1641126814491 0.000045 971268 31523695 3.081073 0.976624284859 0.969189271753 0.999999554404 0.999998962594 0.984594413078 0.975128170336 0.972892573600 0.970667204156 0.947215985975 0.972899675927 0.972899157490 0.000001037406 0.972892054895 0.996084757586 0.993746341306 0.994914175412 0.994914175412
43 FINNISH_RADIXOR FI_FI ALL_WORDS ANY_CANDIDATE 57027 1811717 292 1754389 57328 6 1876272 69769 31523695 0 0 1641126814491 0 1641126814491 0.000000 0 31523695 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
44 FINNISH_RADIXOR FI_FI ALL_WORDS ALL_CANDIDATES 57027 1811717 292 1754389 57328 6 1876272 69769 31523695 1683575 0 1641125130916 1683575 1641126814491 0.000103 0 31523695 0.000000 0.949301011495 1.000000000000 0.999998974135 0.999998974154 0.999999487067 0.959025334376 0.973991195713 0.989431554710 0.949301011495 0.974320794962 0.974320295201 0.000001025846
45 FINNISH_RADIXOR FI_FI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 54762 1757055 274 1757055 0 1 1757055 54633 30078528 730145 735305 1543588714007 730145 1543589444152 0.000047 735305 30813833 2.386282 0.976300667023 0.976137178390 0.999999526982 0.999999050641 0.988068352686 0.976267964916 0.976218915862 0.976169871736 0.953542638154 0.976218919284 0.976218444595 0.000000949359 0.976218441173 0.996000407428 0.996068984852 0.996034694959 0.996034694959
46 FINNISH_RADIXOR FI_FI LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 54762 1757055 274 1712724 44331 6 1805864 54984 30813833 0 0 1543589444152 0 1543589444152 0.000000 0 30813833 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
47 FINNISH_RADIXOR FI_FI LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 54762 1757055 274 1712724 44331 6 1805864 54984 30813833 1653320 0 1543587790832 1653320 1543589444152 0.000107 0 30813833 0.000000 0.949077148834 1.000000000000 0.999998928912 0.999998928933 0.999999464456 0.958842548108 0.973873352732 0.989382907677 0.949077148834 0.974205906795 0.974205385065 0.000001071067
48 FRENCH_LUCENE_FRENCH_LIGHT_STEM_FILTER FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 245918 202782 276403 5251833 90395828427 276403 90396104830 0.000306 5251833 5454615 96.282377 0.423181026117 0.037176226003 0.999996942313 0.999938848002 0.518586584158 0.137547303040 0.068348107452 0.045471618191 0.035383242558 0.125428359900 0.125414592230 0.000061151998 0.068339028277 0.974109647704 0.812375827422 0.885921707253 0.885921707253
49 FRENCH_LUCENE_FRENCH_LIGHT_STEM_FILTER FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 245182 200690 262689 5239869 88711863817 262689 88712126506 0.000296 5239869 5440559 96.311225 0.433101197939 0.036887753630 0.999997038860 0.999937976681 0.518442396245 0.137570562409 0.067985131280 0.045148356975 0.035188720533 0.126396717862 0.126383026150 0.000062023319 0.067976159358 0.975085555241 0.811143708698 0.885591261484 0.885591261484
50 FRENCH_LUCENE_FRENCH_MINIMAL_STEM_FILTER FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 269236 183612 160438 5271003 90395944392 160438 90396104830 0.000177 5271003 5454615 96.633823 0.533678244441 0.033661770812 0.999998225167 0.999939918724 0.516829997990 0.134399775137 0.063329059361 0.041424008382 0.032699958487 0.134031916915 0.134021061615 0.000060081276 0.063322352769 0.984019125555 0.810978546011 0.889158144694 0.889158144694
51 FRENCH_LUCENE_FRENCH_MINIMAL_STEM_FILTER FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 268411 181686 147476 5258873 88711979030 147476 88712126506 0.000166 5258873 5440559 96.660527 0.551965293685 0.033394730211 0.999998337589 0.999939061122 0.516696533900 0.134438681544 0.062979128454 0.041121435592 0.032513396928 0.135767198057 0.135756528540 0.000060938878 0.062972571968 0.985086367216 0.809773733549 0.888868235590 0.888868235590
52 FRENCH_RADIXOR FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 60225 4985455 318767 469160 90395786063 318767 90396104830 0.000353 469160 5454615 8.601157 0.939903156391 0.913988429981 0.999996473664 0.999991284144 0.956992451823 0.934603310507 0.926764667966 0.919056419273 0.863524187383 0.926855226151 0.926850879168 0.000008715856 0.926760310630 0.988772235003 0.985214034569 0.986989927876 0.986989927876
53 FRENCH_RADIXOR FR_FR ALL_WORDS ANY_CANDIDATE 59240 425210 2301 382170 43040 56 477024 60383 5454383 12 232 90396104818 12 90396104830 0.000000 232 5454615 0.004253 0.999997799939 0.999957467209 0.999999999867 0.999999997301 0.999978733538 0.999989733133 0.999977633167 0.999965533495 0.999955267335 0.999977633371 0.999977632021 0.000000002699
54 FRENCH_RADIXOR FR_FR ALL_WORDS ALL_CANDIDATES 59240 425210 2301 382170 43040 56 477024 60383 5454383 1056255 232 90395048575 1056255 90396104830 0.001168 232 5454615 0.004253 0.837764747479 0.999957467209 0.999988315260 0.999988313399 0.999972891234 0.865852951156 0.911703747510 0.962682080453 0.837734895644 0.915275431226 0.915270082203 0.000011686601
55 FRENCH_RADIXOR FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 58069 4975123 315266 465436 88711811240 315266 88712126506 0.000355 465436 5440559 8.554930 0.940407784758 0.914450702584 0.999996446190 0.999991200142 0.957223574387 0.935099145312 0.927247620620 0.919526848134 0.864363145162 0.927338427699 0.927334038919 0.000008799858 0.927243221287 0.988915897225 0.985549842615 0.987230000708 0.987230000708
56 FRENCH_RADIXOR FR_FR LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 57698 421231 2133 380101 41130 56 468574 58208 5440559 0 0 88712126506 0 88712126506 0.000000 0 5440559 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
57 FRENCH_RADIXOR FR_FR LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 57698 421231 2133 380101 41130 56 468574 58208 5440559 938985 0 88711187521 938985 88712126506 0.001058 0 5440559 0.000000 0.852813147774 1.000000000000 0.999989415370 0.999989416019 0.999994707685 0.878679151458 0.920560336911 0.966633773699 0.852813147774 0.923478829088 0.923473941734 0.000010583981
58 GERMAN_CISTEM DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 59097 1053889 477122 329983 44094768857 477122 44095245979 0.001082 329983 1383872 23.844908 0.688361481400 0.761550923785 0.999989179741 0.999981696901 0.880770051763 0.701851885397 0.723108954973 0.745693871888 0.566304351331 0.724031989665 0.724022910459 0.000018303099 0.723099826442 0.974048119240 0.975147027686 0.974597263694 0.974597263694
59 GERMAN_CISTEM DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 23023 725447 156784 147964 11263599558 156784 11263756342 0.001392 147964 873411 16.940936 0.822286906717 0.830590638313 0.999986080665 0.999972946470 0.915288359489 0.823934343933 0.826417914358 0.828916502414 0.704184159310 0.826428343371 0.826414817348 0.000027053530 0.826404386881 0.985935685912 0.973569821618 0.979713735095 0.979713735095
60 GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 98357 709263 205740 674609 44095040239 205740 44095245979 0.000467 674609 1383872 48.747933 0.775148278202 0.512520666651 0.999995334191 0.999980035912 0.756258000421 0.703092101246 0.617052253820 0.549774428024 0.446186239158 0.630301128270 0.630292039259 0.000019964088 0.617042686770 0.980753120457 0.936533167951 0.958133203614 0.958133203614
61 GERMAN_LUCENE_GERMAN_LIGHT_STEM_FILTER DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 50335 471565 55477 401846 11263700865 55477 11263756342 0.000493 401846 873411 46.008809 0.894738939212 0.539911908597 0.999995074734 0.999959401861 0.769953491666 0.790797426464 0.673446377708 0.586423560557 0.507666155661 0.695039717114 0.695022690564 0.000040598139 0.673427319227 0.991320177896 0.915069562866 0.951669957566 0.951669957566
62 GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 140505 271626 110840 1112246 44095135139 110840 44095245979 0.000251 1112246 1383872 80.372029 0.710196461908 0.196279713731 0.999997486350 0.999972263504 0.598138600041 0.466112921692 0.307558349534 0.229493166050 0.181724639931 0.373359288402 0.373350267608 0.000027736496 0.307548938689 0.983615403456 0.896263607272 0.937910029995 0.937910029995
63 GERMAN_LUCENE_GERMAN_MINIMAL_STEM_FILTER DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 80363 132221 21214 741190 11263735128 21214 11263756342 0.000188 741190 873411 84.861537 0.861739498811 0.151384628772 0.999998116614 0.999932318770 0.575691372693 0.444544636019 0.257528392768 0.181269722976 0.147794886125 0.361184321539 0.361168285320 0.000067681230 0.257511188319 0.992642524078 0.854402700840 0.918349417869 0.918349417869
64 GERMAN_LUCENE_GERMAN_STEM_FILTER DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 81085 619354 331871 764518 44094914108 331871 44095245979 0.000753 764518 1383872 55.244849 0.651111987174 0.447551507654 0.999992473769 0.999975136671 0.723771990712 0.596821367368 0.530473894660 0.477402037056 0.360982967729 0.539820480819 0.539808754751 0.000024863329 0.530461889452 0.975549631706 0.942889706548 0.958941664321 0.958941664321
65 GERMAN_LUCENE_GERMAN_STEM_FILTER DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 41574 378734 78723 494677 11263677619 78723 11263756342 0.000699 494677 873411 56.637368 0.827911694432 0.433626322545 0.999993010946 0.999949097306 0.716809666745 0.700518896035 0.569153364571 0.479276535831 0.397773842757 0.599169678345 0.599148958371 0.000050902694 0.569130398173 0.988583531594 0.918717729459 0.952371013508 0.952371013508
66 GERMAN_RADIXOR DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 68104 1128969 98192 254903 44095147787 98192 44095245979 0.000223 254903 1383872 18.419550 0.919984419322 0.815804496370 0.999997773184 0.999991992699 0.907901134777 0.897072808397 0.864768082211 0.834709150216 0.761754553110 0.866329859738 0.866325955582 0.000008007301 0.864764092865 0.989946248415 0.975085217969 0.982459538105 0.982459538105
67 GERMAN_RADIXOR DE_DE ALL_WORDS ANY_CANDIDATE 54092 296974 1474 248400 48574 8 361016 70717 1272705 1375 111167 44095244604 1375 44095245979 0.000003 111167 1383872 8.033041 0.998920789903 0.919669593720 0.999999968818 0.999997447832 0.959834781269 0.981996366774 0.957658377578 0.934497606897 0.918756727140 0.958476435291 0.958475209548 0.000002552168
68 GERMAN_RADIXOR DE_DE ALL_WORDS ALL_CANDIDATES 54092 296974 1474 248400 48574 8 361016 70717 1272705 244817 111167 44095001162 244817 44095245979 0.000555 111167 1383872 8.033041 0.838673179038 0.919669593720 0.999994447996 0.999991927184 0.959832020858 0.853710645080 0.877305874349 0.902242446842 0.781429112618 0.878238135035 0.878234164088 0.000008072816
69 GERMAN_RADIXOR DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 17264 814297 47898 59114 11263708444 47898 11263756342 0.000425 59114 873411 6.768177 0.944446441930 0.932318232768 0.999995747600 0.999990500176 0.966156990184 0.941995622128 0.938343149309 0.934718891125 0.883847872972 0.938362743125 0.938357995965 0.000009499824 0.938338399230 0.994062310308 0.990664418294 0.992360455671 0.992360455671
70 GERMAN_RADIXOR DE_DE LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 16007 150098 228 135120 14978 8 167157 18366 873411 0 0 11263756342 0 11263756342 0.000000 0 873411 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
71 GERMAN_RADIXOR DE_DE LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 16007 150098 228 135120 14978 8 167157 18366 873411 97544 0 11263658798 97544 11263756342 0.000866 0 873411 0.000000 0.899538083639 1.000000000000 0.999991340012 0.999991340683 0.999995670006 0.917982540684 0.947112449481 0.978151677228 0.899538083639 0.948439815507 0.948435708759 0.000008659317
72 HE_IL_RADIXOR HE_IL ALL_WORDS PRIMARY_OUTPUT 2358 58714 0 58714 0 1 58714 2358 688361 25234 19916 1722904030 25234 1722929264 0.001465 19916 708277 2.811894 0.964638205144 0.971881057835 0.999985354013 0.999973805398 0.985933205924 0.966078126522 0.968246086849 0.970423799230 0.938446730860 0.968252859146 0.968239762122 0.000026194602 0.968232984327 0.993166390361 0.993627509527 0.993396896433 0.993396896433
73 HE_IL_RADIXOR HE_IL ALL_WORDS ANY_CANDIDATE 2358 58714 0 56674 2040 40 62376 2358 708277 0 0 1722929264 0 1722929264 0.000000 0 708277 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
74 HE_IL_RADIXOR HE_IL ALL_WORDS ALL_CANDIDATES 2358 58714 0 56674 2040 40 62376 2358 708277 86628 0 1722842636 86628 1722929264 0.005028 0 708277 0.000000 0.891020939609 1.000000000000 0.999949720513 0.999949741174 0.999974860256 0.910874182109 0.942370251906 0.976122467036 0.891020939609 0.943939055029 0.943915324345 0.000050258826
75 HE_IL_RADIXOR HE_IL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 2358 58714 0 58714 0 1 58714 2358 688361 25234 19916 1722904030 25234 1722929264 0.001465 19916 708277 2.811894 0.964638205144 0.971881057835 0.999985354013 0.999973805398 0.985933205924 0.966078126522 0.968246086849 0.970423799230 0.938446730860 0.968252859146 0.968239762122 0.000026194602 0.968232984327 0.993166390361 0.993627509527 0.993396896433 0.993396896433
76 HE_IL_RADIXOR HE_IL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 2358 58714 0 56674 2040 40 62376 2358 708277 0 0 1722929264 0 1722929264 0.000000 0 708277 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
77 HE_IL_RADIXOR HE_IL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 2358 58714 0 56674 2040 40 62376 2358 708277 86628 0 1722842636 86628 1722929264 0.005028 0 708277 0.000000 0.891020939609 1.000000000000 0.999949720513 0.999949741174 0.999974860256 0.910874182109 0.942370251906 0.976122467036 0.891020939609 0.943939055029 0.943915324345 0.000050258826
78 HUNGARIAN_LUCENE_HUNGARIAN_LIGHT_STEM_FILTER HU_HU ALL_WORDS PRIMARY_OUTPUT 19406 916344 1 916344 0 1 916344 94328 14036270 4132555 8125833 419816410338 4132555 419820542893 0.000984 8125833 22162103 36.665442 0.772546931351 0.633345580968 0.999990156377 0.999970802427 0.816667868673 0.740017627855 0.696054898613 0.657022705053 0.533806904809 0.699492090778 0.699477892426 0.000029197573 0.696040442258 0.982615378770 0.926771756762 0.953876941828 0.953876941828
79 HUNGARIAN_LUCENE_HUNGARIAN_LIGHT_STEM_FILTER HU_HU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 18360 878513 1 878513 0 1 878513 91516 13492703 3639046 7918708 385867055871 3639046 385870694917 0.000943 7918708 21411411 36.983588 0.787584676848 0.630164121365 0.999990569261 0.999970049260 0.815077345313 0.750107959995 0.700134758022 0.656404225003 0.538621031944 0.704491026122 0.704476576404 0.000029950740 0.700119966552 0.983686881315 0.925487153321 0.953699929950 0.953699929950
80 HUNGARIAN_RADIXOR HU_HU ALL_WORDS PRIMARY_OUTPUT 19406 916344 1 916344 0 1 916344 20535 21962266 272900 199837 419820269993 272900 419820542893 0.000065 199837 22162103 0.901706 0.987726648859 0.990982940563 0.999999349960 0.999998874014 0.995491145262 0.988376194087 0.989352115329 0.990329965723 0.978928596533 0.989353455019 0.989352892139 0.000001125986 0.989351552308 0.998036093538 0.997808712909 0.997922390271 0.997922390271
81 HUNGARIAN_RADIXOR HU_HU ALL_WORDS ANY_CANDIDATE 19406 916344 1 904024 12320 5 929326 20567 22162103 0 0 419820542893 0 419820542893 0.000000 0 22162103 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
82 HUNGARIAN_RADIXOR HU_HU ALL_WORDS ALL_CANDIDATES 19406 916344 1 904024 12320 5 929326 20567 22162103 460158 0 419820082735 460158 419820542893 0.000110 0 22162103 0.000000 0.979659062372 1.000000000000 0.999998903917 0.999998903975 0.999999451959 0.983660778882 0.989725029923 0.995864516790 0.979659062372 0.989777279176 0.989776736737 0.000001096025
83 HUNGARIAN_RADIXOR HU_HU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 18360 878513 1 878513 0 1 878513 18363 21247134 272775 164277 385870422142 272775 385870694917 0.000071 164277 21411411 0.767240 0.987324528185 0.992327595785 0.999999293092 0.999998867424 0.996163444439 0.988321101757 0.989819739994 0.991322930046 0.979844666523 0.989822900984 0.989822335019 0.000001132576 0.989819173678 0.997945135090 0.998273386381 0.998109233747 0.998109233747
84 HUNGARIAN_RADIXOR HU_HU LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 18360 878513 1 867360 11153 5 890245 18375 21411411 0 0 385870694917 0 385870694917 0.000000 0 21411411 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
85 HUNGARIAN_RADIXOR HU_HU LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 18360 878513 1 867360 11153 5 890245 18375 21411411 458462 0 385870236455 458462 385870694917 0.000119 0 21411411 0.000000 0.979036823854 1.000000000000 0.999998811877 0.999998811943 0.999999405938 0.983158850285 0.989407384494 0.995735852714 0.979036823854 0.989462896653 0.989462308851 0.000001188057
86 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ ALL_WORDS PRIMARY_OUTPUT 5113 51676 2 51676 0 1 51676 10920 213552 11408 88283 1334865407 11408 1334876815 0.000855 88283 301835 29.248762 0.949288762447 0.707512382593 0.999991453893 0.999925335085 0.853751918243 0.888559718726 0.810759403563 0.745486280807 0.681745481942 0.819532521678 0.819499025505 0.000074664915 0.810722859062 0.995776551361 0.952852006662 0.973841506371 0.973841506371
87 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ ALL_WORDS ANY_CANDIDATE 5113 51676 2 48359 3317 5 55596 11359 224312 10102 77523 1334866713 10102 1334876815 0.000757 77523 301835 25.683900 0.956905304291 0.743160998559 0.999992432261 0.999934372078 0.871576715410 0.904855299474 0.836596431881 0.777913569166 0.719093919606 0.843288029954 0.843258147533 0.000065627922
88 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ ALL_WORDS ALL_CANDIDATES 5113 51676 2 48359 3317 5 55596 11359 224312 13917 77523 1334862898 13917 1334876815 0.001043 77523 301835 25.683900 0.941581419558 0.743160998559 0.999989574319 0.999931514783 0.871575286439 0.893850652440 0.830686733424 0.775860578084 0.710405634802 0.836508570179 0.836476906392 0.000068485217
89 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 5038 50968 2 50968 0 1 50968 10816 210827 11239 87986 1298532976 11239 1298544215 0.000866 87986 298813 29.445171 0.949388920411 0.705548286052 0.999991344923 0.999923605087 0.852769815487 0.888008949714 0.809504702628 0.743753342581 0.679973036781 0.818437368155 0.818403143837 0.000076394913 0.809467327086 0.995812473772 0.952393750423 0.973619286126 0.973619286126
90 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 5038 50968 2 47731 3237 5 54804 11240 221382 10028 77431 1298534187 10028 1298544215 0.000772 77431 298813 25.912862 0.956665658355 0.740871381098 0.999992277506 0.999932663918 0.870431829302 0.904003665310 0.835052421340 0.775874033233 0.716815448726 0.841882537861 0.841851913927 0.000067336082
91 HUNSPELL_CZECH_LUCENE_FILTER CS_CZ LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 5038 50968 2 47731 3237 5 54804 11240 221382 13601 77431 1298530614 13601 1298544215 0.001047 77431 298813 25.912862 0.942119217135 0.740871381098 0.999989525963 0.999929913009 0.870430453530 0.893573737936 0.829462940899 0.773935751817 0.708617411512 0.835457458856 0.835425115185 0.000070086991
92 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL ALL_WORDS PRIMARY_OUTPUT 4992 26477 85 26477 0 1 26477 15909 18482 1333 46084 350436627 1333 350437960 0.000380 46084 64566 71.375027 0.932727731517 0.286249728960 0.999996196188 0.999864717095 0.643122962574 0.642512480358 0.438060700869 0.332315636923 0.280459491039 0.516713712165 0.516673857221 0.000135282905 0.438012080403 0.996931617211 0.889026094124 0.939891935467 0.939891935467
93 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL ALL_WORDS ANY_CANDIDATE 4992 26477 85 25223 1254 3 27763 16027 21374 1164 43192 350436796 1164 350437960 0.000332 43192 64566 66.895889 0.948353891206 0.331041105226 0.999996678442 0.999873450270 0.665518891834 0.690740573172 0.490769654666 0.380588457347 0.325178761600 0.560307166017 0.560267948638 0.000126549730
94 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL ALL_WORDS ALL_CANDIDATES 4992 26477 85 25223 1254 3 27763 16027 21374 1738 43192 350436222 1738 350437960 0.000496 43192 64566 66.895889 0.924800969193 0.331041105226 0.999995040492 0.999871812621 0.665518072859 0.680639942935 0.487556741714 0.379812066416 0.322363658301 0.553305643343 0.553264631551 0.000128187379
95 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4796 25678 84 25678 0 1 25678 15258 18333 1310 44814 329602546 1310 329603856 0.000397 44814 63147 70.967742 0.933309575930 0.290322580645 0.999996025532 0.999860089122 0.645159303089 0.646808120294 0.442879574828 0.336717714000 0.284422172921 0.520538994337 0.520497519470 0.000139910878 0.442828931093 0.996884174988 0.889060613994 0.939890140871 0.939890140871
96 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 4796 25678 84 24492 1186 3 26896 15323 21212 1141 41935 329602715 1141 329603856 0.000346 41935 63147 66.408539 0.948955397486 0.335914611937 0.999996538269 0.999869334815 0.667955575103 0.695206444720 0.496187134503 0.385755489360 0.329952712792 0.564595416287 0.564554662442 0.000130665185
97 HUNSPELL_DUTCH_LUCENE_FILTER NL_NL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 4796 25678 84 24492 1186 3 26896 15323 21212 1712 41935 329602144 1712 329603856 0.000519 41935 63147 66.408539 0.925318443553 0.335914611937 0.999994805886 0.999867602764 0.667954708912 0.684951854459 0.492895400309 0.384956009176 0.327047903915 0.557519493726 0.557476858392 0.000132397236
98 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK ALL_WORDS PRIMARY_OUTPUT 396939 607439 250964 607439 0 1 607439 557518 46002 1981986 267868 184488469785 1981986 184490451771 0.001074 267868 313870 85.343614 0.022683566175 0.146563864020 0.999989256972 0.999987805059 0.573276560496 0.027298226808 0.039286754363 0.070050933952 0.020036970960 0.057659267324 0.057655308782 0.000012194941 0.039283923590 0.993096189204 0.963677172906 0.978165531652 0.978165531652
99 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK ALL_WORDS ANY_CANDIDATE 396939 607439 250964 600602 6837 4 614296 557638 51229 1978852 262641 184488472919 1978852 184490451771 0.001073 262641 313870 83.678274 0.025234953679 0.163217255552 0.999989273960 0.999987850378 0.581603264756 0.030369825498 0.043711664621 0.077960810954 0.022344183028 0.064177721084 0.064173802046 0.000012149622
100 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK ALL_WORDS ALL_CANDIDATES 396939 607439 250964 600602 6837 4 614296 557638 51229 2008917 262641 184488442854 2008917 184490451771 0.001089 262641 313870 83.678274 0.024866684206 0.163217255552 0.999989110997 0.999987687416 0.581603183275 0.029942881217 0.043158091605 0.077253888104 0.022054971033 0.063707707153 0.063703758400 0.000012312584
101 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 374384 583910 228735 583910 0 1 583910 535362 45926 1978041 265965 170472862163 1978041 170474840204 0.001160 265965 311891 85.274984 0.022691081426 0.147250161114 0.999988396874 0.999986836756 0.573619278994 0.027311677226 0.039322595808 0.070190378755 0.020055617372 0.057803679777 0.057799415161 0.000013163244 0.039319549964 0.993066314983 0.962316521867 0.977449637185 0.977449637185
102 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 374384 583910 228735 577124 6786 4 590716 535485 51150 1974950 260741 170472865254 1974950 170474840204 0.001158 260741 311891 83.600040 0.025245545630 0.163999602425 0.999988415006 0.999986885532 0.581994008716 0.030387494919 0.043755514884 0.078123472659 0.022367099418 0.064344847861 0.064340626006 0.000013114468
103 HUNSPELL_ENGLISH_LUCENE_FILTER US_UK LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 374384 583910 228735 577124 6786 4 590716 535485 51150 2004598 260741 170472835606 2004598 170474840204 0.001176 260741 311891 83.600040 0.024881454342 0.163999602425 0.999988241092 0.999986711618 0.581993921758 0.029965261387 0.043207600483 0.077422296168 0.022080830084 0.063879172034 0.063874918547 0.000013288382
104 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 154336 3422734 776728 2031881 90395328102 776728 90396104830 0.000859 2031881 5454615 37.250677 0.815041069547 0.627493232795 0.999991407506 0.999968931852 0.813742320150 0.769068574566 0.709075347131 0.657764674673 0.549277098051 0.715145268872 0.715130511338 0.000031068148 0.709060074832 0.978337247291 0.913705954219 0.944917713687 0.944917713687
105 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR ALL_WORDS ANY_CANDIDATE 59240 425210 2301 411699 13511 4 439015 154718 3610612 745831 1844003 90395358999 745831 90396104830 0.000825 1844003 5454615 33.806291 0.828798173189 0.661937093635 0.999991749302 0.999971351888 0.830964421468 0.789018996925 0.736029080656 0.689708764155 0.582314885091 0.740683639600 0.740669908387 0.000028648112
106 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR ALL_WORDS ALL_CANDIDATES 59240 425210 2301 411699 13511 4 439015 154718 3610612 1043199 1844003 90395061631 1043199 90396104830 0.001154 1844003 5454615 33.806291 0.775839843947 0.661937093635 0.999988459691 0.999968062476 0.830962776663 0.750027659074 0.714376699201 0.681961135862 0.555665643861 0.716629033342 0.716613365354 0.000031937524
107 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 153822 3412548 763305 2028011 88711363201 763305 88712126506 0.000860 2028011 5440559 37.275784 0.817209801207 0.627242163903 0.999991395708 0.999968537054 0.813616779806 0.770536594362 0.709734150326 0.657825640123 0.550068151075 0.715952822518 0.715937898033 0.000031462946 0.709718690125 0.979328164393 0.913161860024 0.945088340370 0.945088340370
108 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 57698 421231 2133 407794 13437 4 434961 154205 3600083 733584 1840476 88711392922 733584 88712126506 0.000827 1840476 5440559 33.828803 0.830724418835 0.661711967465 0.999991730736 0.999970985904 0.830851849101 0.790350629656 0.736648201095 0.689779349655 0.583090317150 0.741417756470 0.741403865778 0.000029014096
109 HUNSPELL_FRENCH_LUCENE_FILTER FR_FR LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 57698 421231 2133 407794 13437 4 434961 154205 3600083 1027635 1840476 88711098871 1027635 88712126506 0.001158 1840476 5440559 33.828803 0.777939148410 0.661711967465 0.999988416071 0.999967671442 0.830850191768 0.751538185756 0.715133880405 0.682093458746 0.556582409247 0.717475884237 0.717460037184 0.000032328558
110 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 182774 391862 203883 992010 44095042096 203883 44095245979 0.000462 992010 1383872 71.683653 0.657768004767 0.283163471766 0.999995376304 0.999972880172 0.641579424035 0.520145203475 0.395896782054 0.319562150060 0.246802560848 0.431573715426 0.431562811676 0.000027119828 0.395885371175 0.980462638581 0.886872825924 0.931322397630 0.931322397630
111 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE ALL_WORDS ANY_CANDIDATE 54092 296974 1474 289083 7891 3 305052 183111 408175 158403 975697 44095087576 158403 44095245979 0.000359 975697 1383872 70.504859 0.720421548313 0.294951411691 0.999996407708 0.999974281481 0.647473909700 0.559115650060 0.418544438463 0.334456395588 0.264657729653 0.460965674088 0.460955788036 0.000025718519
112 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE ALL_WORDS ALL_CANDIDATES 54092 296974 1474 289083 7891 3 305052 183111 408175 242551 975697 44095003428 242551 44095245979 0.000550 975697 1383872 70.504859 0.627260936247 0.294951411691 0.999994499384 0.999972373218 0.647472955538 0.511911128190 0.401234052132 0.329906951166 0.250964847398 0.430129630047 0.430118032816 0.000027626782
113 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 86983 278093 84679 595318 11263671663 84679 11263756342 0.000752 595318 873411 68.160122 0.766577905682 0.318398783620 0.999992482170 0.999939634323 0.659195632895 0.598178360154 0.449922058465 0.360558871242 0.290257700216 0.494041974653 0.494019111671 0.000060365677 0.449897024669 0.988041339480 0.865580709092 0.922765807515 0.922765807515
114 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 16007 150098 228 145109 4989 3 155207 87393 288864 60996 584547 11263695346 60996 11263756342 0.000542 584547 873411 66.926911 0.825655976676 0.330730893016 0.999994584755 0.999942692923 0.665362738886 0.635466205220 0.472281285177 0.375782098835 0.309141519702 0.522560942370 0.522540242219 0.000057307077
115 HUNSPELL_GERMAN_LUCENE_FILTER DE_DE LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 16007 150098 228 145109 4989 3 155207 87393 288864 96545 584547 11263659797 96545 11263756342 0.000857 584547 873411 66.926911 0.749499881944 0.330730893016 0.999991428703 0.999939537116 0.665361160860 0.598050472724 0.458944090497 0.372338300095 0.297811447117 0.497878263505 0.497854575726 0.000060462884
116 HUNSPELL_POLISH_LUCENE_FILTER PL_PL ALL_WORDS PRIMARY_OUTPUT 9990 122341 1 122341 0 1 122341 18419 971262 52652 149705 7482425351 52652 7482478003 0.000704 149705 1120967 13.354987 0.948577712581 0.866450127435 0.999992963294 0.999972959935 0.933221545364 0.930929837176 0.905655838249 0.881717903868 0.827578626454 0.906584403102 0.906571161039 0.000027040065 0.905642343969 0.994545991966 0.970520439141 0.982386343372 0.982386343372
117 HUNSPELL_POLISH_LUCENE_FILTER PL_PL ALL_WORDS ANY_CANDIDATE 9990 122341 1 110894 11447 6 135231 19068 1040224 42213 80743 7482435790 42213 7482478003 0.000564 80743 1120967 7.202977 0.961001887408 0.927970225707 0.999994358420 0.999983569937 0.963982292063 0.954208759768 0.944197251162 0.934393641743 0.894293230626 0.944341642819 0.944333470354 0.000016430063
118 HUNSPELL_POLISH_LUCENE_FILTER PL_PL ALL_WORDS ALL_CANDIDATES 9990 122341 1 110894 11447 6 135231 19068 1040224 82745 80743 7482395258 82745 7482478003 0.001106 80743 1120967 7.202977 0.926315864463 0.927970225707 0.999988941498 0.999978153827 0.963979583602 0.926646264647 0.927142307089 0.927638880888 0.864180136112 0.927142676087 0.927131751477 0.000021846173
119 HUNSPELL_POLISH_LUCENE_FILTER PL_PL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 9846 120925 1 120925 0 1 120925 18149 965984 51950 148667 7310200749 51950 7310252699 0.000711 148667 1114651 13.337538 0.948965257080 0.866624620621 0.999992893543 0.999972560946 0.933308757082 0.931268723294 0.905927782480 0.881929423296 0.828032892137 0.906860880124 0.906847444801 0.000027439054 0.905914089179 0.994583905165 0.970514203019 0.982401644006 0.982401644006
120 HUNSPELL_POLISH_LUCENE_FILTER PL_PL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 9846 120925 1 109660 11265 6 133595 18789 1034283 41671 80368 7310211028 41671 7310252699 0.000570 80368 1114651 7.210149 0.961270649117 0.927898508143 0.999994299650 0.999983308321 0.963946403896 0.954405554191 0.944289819479 0.934386268967 0.894459328803 0.944437187555 0.944428885928 0.000016691679
121 HUNSPELL_POLISH_LUCENE_FILTER PL_PL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 9846 120925 1 109660 11265 6 133595 18789 1034283 81865 80368 7310170834 81865 7310252699 0.001120 80368 1114651 7.210149 0.926653992123 0.927898508143 0.999988801345 0.999977810854 0.963943654744 0.926902628188 0.927275832560 0.927649337585 0.864412176686 0.927276041347 0.927264945147 0.000022189146
122 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 495840 9662476 536192 32310860 379566781918 536192 379567318110 0.000141 32310860 41973336 76.979490 0.947425291224 0.230205099733 0.999998587360 0.999913471422 0.615101843546 0.583708381625 0.370408466579 0.271277635967 0.227301418167 0.467014061518 0.466991649518 0.000086528578 0.370381248953 0.993314263125 0.790558492734 0.880413722434 0.880413722434
123 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES ALL_WORDS ANY_CANDIDATE 65059 871332 3589 853455 17877 5 890999 496361 10079118 416345 31894218 379566901765 416345 379567318110 0.000110 31894218 41973336 75.986855 0.960330954432 0.240131449166 0.999998903106 0.999914884689 0.620065176136 0.600267728541 0.384194728757 0.282504215637 0.237772914592 0.480214185303 0.480192080762 0.000085115311
124 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES ALL_WORDS ALL_CANDIDATES 65059 871332 3589 853455 17877 5 890999 496361 10079118 888077 31894218 379566430033 888077 379567318110 0.000234 31894218 41973336 75.986855 0.919024235459 0.240131449166 0.999997660291 0.999913642011 0.620064554728 0.587073016700 0.380771322449 0.281759130783 0.235155989841 0.469772946730 0.469749183448 0.000086357989
125 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 495045 9628515 531181 32234855 377860138584 531181 377860669765 0.000141 32234855 41863370 77.000144 0.947716841134 0.229998564377 0.999998594241 0.999913295008 0.614998579309 0.583531128169 0.370163304101 0.271052948234 0.227116805648 0.466876335765 0.466853897372 0.000086704992 0.370136051001 0.993362468962 0.790499503024 0.880396073652 0.880396073652
126 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 64918 869371 3525 851564 17807 5 888962 495572 10041262 412198 31822108 377860257567 412198 377860669765 0.000109 31822108 41863370 76.014205 0.960568271175 0.239857947413 0.999998909127 0.999914702064 0.619928428270 0.599999808789 0.383863548307 0.282205460900 0.237519268813 0.479999931119 0.479977799265 0.000085297936
127 HUNSPELL_SPANISH_LUCENE_FILTER ES_ES LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 64918 869371 3525 851564 17807 5 888962 495572 10041262 878949 31822108 377859790816 878949 377860669765 0.000233 31822108 41863370 76.014205 0.919511720057 0.239857947413 0.999997673881 0.999913466955 0.619927810647 0.586904802235 0.380469146267 0.281467012980 0.234925531298 0.469629847641 0.469606065497 0.000086533045
128 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA ALL_WORDS PRIMARY_OUTPUT 1493 14245 4 14245 0 1 14245 3137 50416 794 14924 101386756 794 101387550 0.000783 14924 65340 22.840526 0.984495215778 0.771594735231 0.999992168664 0.999845070949 0.885793451947 0.933007624547 0.865139425139 0.806475349522 0.762331024889 0.871568313648 0.871498740996 0.000154929051 0.865063055969 0.998114340300 0.949803904722 0.973360047526 0.973360047526
129 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA ALL_WORDS ANY_CANDIDATE 1493 14245 4 12923 1322 6 15740 3311 55875 326 9465 101387224 326 101387550 0.000322 9465 65340 14.485767 0.994199391470 0.855142332415 0.999996784615 0.999903492153 0.927569558515 0.962883947281 0.919442821764 0.879752236578 0.850896963421 0.922053136488 0.922008115880 0.000096507847
130 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA ALL_WORDS ALL_CANDIDATES 1493 14245 4 12923 1322 6 15740 3311 55875 1271 9465 101386279 1271 101387550 0.001254 9465 65340 14.485767 0.977758723270 0.855142332415 0.999987463944 0.999894177485 0.927564898180 0.950500809733 0.912349166435 0.877142031861 0.838825419225 0.914397547654 0.914347195556 0.000105822515
131 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 1491 14236 4 14236 0 1 14236 3134 50404 794 14920 101258612 794 101259406 0.000784 14920 65324 22.839998 0.984491581702 0.771600024493 0.999992158753 0.999844914465 0.885796091623 0.933006560145 0.865141346698 0.806479484406 0.762334008893 0.871569692311 0.871500048785 0.000155085535 0.865064900251 0.998112856419 0.949788155938 0.973351072054 0.973351072054
132 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 1491 14236 4 12915 1321 6 15730 3308 55859 326 9465 101259080 326 101259406 0.000322 9465 65324 14.489315 0.994197739610 0.855106851999 0.999996780546 0.999903370085 0.927551816273 0.962873710629 0.919421606630 0.879721936116 0.850860624524 0.922033242016 0.921988165267 0.000096629915
133 HUNSPELL_UKRAINIAN_LUCENE_FILTER UK_UA LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 1491 14236 4 12915 1321 6 15730 3308 55859 1271 9465 101258135 1271 101259406 0.001255 9465 65324 14.489315 0.977752494311 0.855106851999 0.999987448080 0.999894043636 0.927547150039 0.950487333415 0.912326261290 0.877111165546 0.838786695698 0.914375665383 0.914325250217 0.000105956364
134 ITALIAN_LUCENE_ITALIAN_LIGHT_STEM_FILTER IT_IT ALL_WORDS PRIMARY_OUTPUT 10009 327551 0 327551 0 1 327551 244870 109684 10589 6034130 53638510622 10589 53638521211 0.000020 6034130 6143814 98.214725 0.911958627456 0.017852754006 0.999999802586 0.999887319289 0.508926278296 0.082781551919 0.035019947839 0.022207258650 0.017822037328 0.127596916262 0.127588341500 0.000112680711 0.035015703871 0.997481424185 0.737537113266 0.848036553212 0.848036553212
135 ITALIAN_LUCENE_ITALIAN_LIGHT_STEM_FILTER IT_IT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 10007 327469 0 327469 0 1 327469 244808 109658 10588 6032516 53611656484 10588 53611667072 0.000020 6032516 6142174 98.214671 0.911947174958 0.017853287777 0.999999802506 0.999887292971 0.508926545141 0.082783771729 0.035020966336 0.022207918023 0.017822564890 0.127598022525 0.127589445488 0.000112707029 0.035016721203 0.997481197125 0.737534145120 0.848034509070 0.848034509070
136 ITALIAN_RADIXOR IT_IT ALL_WORDS PRIMARY_OUTPUT 10009 327551 0 327551 0 1 327551 10010 6100906 124172 42908 53638397039 124172 53638521211 0.000231 42908 6143814 0.698394 0.980052940702 0.993016064614 0.999997685022 0.999996885431 0.996506874818 0.982618418699 0.986491918597 0.990396078172 0.973343909830 0.986513210398 0.986511657877 0.000003114569 0.986490361200 0.995780270704 0.997112550353 0.996445965204 0.996445965204
137 ITALIAN_RADIXOR IT_IT ALL_WORDS ANY_CANDIDATE 10009 327551 0 321297 6254 4 334175 10012 6143734 0 80 53638521211 0 53638521211 0.000000 80 6143814 0.001302 1.000000000000 0.999986978772 1.000000000000 0.999999998509 0.999993489386 0.999997395727 0.999993489344 0.999989582991 0.999986978772 0.999993489365 0.999993488619 0.000000001491
138 ITALIAN_RADIXOR IT_IT ALL_WORDS ALL_CANDIDATES 10009 327551 0 321297 6254 4 334175 10012 6143734 170950 80 53638350261 170950 53638521211 0.000319 80 6143814 0.001302 0.972928178195 0.999986978772 0.999996812925 0.999996811799 0.999991895849 0.978222150749 0.986272020913 0.994455476443 0.972915852437 0.986364795335 0.986363222748 0.000003188201
139 ITALIAN_RADIXOR IT_IT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 10007 327469 0 327469 0 1 327469 10007 6099346 124171 42828 53611542901 124171 53611667072 0.000232 42828 6142174 0.697278 0.980048098206 0.993027224563 0.999997683881 0.999996885382 0.996512454222 0.982616709845 0.986494972258 0.990403969991 0.973349855458 0.986516316590 0.986514764059 0.000003114618 0.986493414837 0.995779575755 0.997114909855 0.996446795437 0.996446795437
140 ITALIAN_RADIXOR IT_IT LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 10007 327469 0 321217 6252 4 334089 10007 6142174 0 0 53611667072 0 53611667072 0.000000 0 6142174 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
141 ITALIAN_RADIXOR IT_IT LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 10007 327469 0 321217 6252 4 334089 10007 6142174 170949 0 53611496123 170949 53611667072 0.000319 0 6142174 0.000000 0.972921642743 1.000000000000 0.999996811347 0.999996811712 0.999998405674 0.978219357390 0.986274996092 0.994464412864 0.972921642743 0.986367904356 0.986366331762 0.000003188288
142 NL_NL_RADIXOR NL_NL ALL_WORDS PRIMARY_OUTPUT 4992 26477 85 26477 0 1 26477 5015 63102 1214 1464 350436746 1214 350437960 0.000346 1464 64566 2.267447 0.981124448038 0.977325527367 0.999996535763 0.999992359542 0.988661031565 0.980362303079 0.979221303208 0.978082956166 0.959288537549 0.979223145453 0.979219325206 0.000007640458 0.979217482290 0.997464133435 0.997003025118 0.997233525974 0.997233525974
143 NL_NL_RADIXOR NL_NL ALL_WORDS ANY_CANDIDATE 4992 26477 85 25905 572 3 27061 5016 64566 0 0 350437960 0 350437960 0.000000 0 64566 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
144 NL_NL_RADIXOR NL_NL ALL_WORDS ALL_CANDIDATES 4992 26477 85 25905 572 3 27061 5016 64566 2651 0 350435309 2651 350437960 0.000756 0 64566 0.000000 0.960560572474 1.000000000000 0.999992435180 0.999992436574 0.999996217590 0.968197604323 0.979883596519 0.991855131329 0.960560572474 0.980081921308 0.980078214229 0.000007563426
145 NL_NL_RADIXOR NL_NL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4796 25678 84 25678 0 1 25678 4797 61763 1214 1384 329602642 1214 329603856 0.000368 1384 63147 2.191711 0.980723121139 0.978082885964 0.999996316791 0.999992119320 0.989039601378 0.980193934392 0.979401224192 0.978609795129 0.959633939808 0.979402113872 0.979398173122 0.000007880680 0.979397283106 0.997373193672 0.997139403762 0.997256285015 0.997256285015
146 NL_NL_RADIXOR NL_NL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 4796 25678 84 25129 549 3 26239 4797 63147 0 0 329603856 0 329603856 0.000000 0 63147 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
147 NL_NL_RADIXOR NL_NL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 4796 25678 84 25129 549 3 26239 4797 63147 2651 0 329601205 2651 329603856 0.000804 0 63147 0.000000 0.959710021581 1.000000000000 0.999991957012 0.999991958552 0.999995978506 0.967506182222 0.979440846873 0.991673628866 0.959710021581 0.979647906945 0.979643967288 0.000008041448
148 NN_NO_RADIXOR NN_NO ALL_WORDS PRIMARY_OUTPUT 4688 18250 23 18250 0 1 18250 4680 26716 6230 3936 166485243 6230 166491473 0.003742 3936 30652 12.840924 0.810902689249 0.871590760799 0.999962580666 0.999938951055 0.935776670732 0.822354650447 0.840152206044 0.858737158800 0.724364188493 0.840699287413 0.840668985911 0.000061048945 0.840121715471 0.983845117159 0.986801676045 0.985321178741 0.985321178741
149 NN_NO_RADIXOR NN_NO ALL_WORDS ANY_CANDIDATE 4688 18250 23 15846 2404 5 21513 4693 30652 0 0 166491473 0 166491473 0.000000 0 30652 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
150 NN_NO_RADIXOR NN_NO ALL_WORDS ALL_CANDIDATES 4688 18250 23 15846 2404 5 21513 4693 30652 13214 0 166478259 13214 166491473 0.007937 0 30652 0.000000 0.698764418912 1.000000000000 0.999920632572 0.999920647181 0.999960316286 0.743561877778 0.822673716418 0.920624241623 0.698764418912 0.835921299473 0.835888126353 0.000079352819
151 NN_NO_RADIXOR NN_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4681 18219 23 18219 0 1 18219 4668 26671 6230 3924 165920046 6230 165926276 0.003755 3924 30595 12.825625 0.810644053372 0.871743748979 0.999962453204 0.999938815429 0.935853101091 0.822169063928 0.840084414766 0.858797921188 0.724263408011 0.840638974932 0.840608609146 0.000061184571 0.840053856992 0.983814668550 0.986841730061 0.985325874420 0.985325874420
152 NN_NO_RADIXOR NN_NO LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 4681 18219 23 15820 2399 5 21477 4681 30595 0 0 165926276 0 165926276 0.000000 0 30595 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
153 NN_NO_RADIXOR NN_NO LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 4681 18219 23 15820 2399 5 21477 4681 30595 13214 0 165913062 13214 165926276 0.007964 0 30595 0.000000 0.698372480541 1.000000000000 0.999920362222 0.999920376903 0.999960181111 0.743206805583 0.822402021397 0.920488118949 0.698372480541 0.835686831618 0.835653554835 0.000079623097
154 NORWEGIAN_BOKMAL_LUCENE_NORWEGIAN_LIGHT_STEM_FILTER NB_NO ALL_WORDS PRIMARY_OUTPUT 17929 75310 252 75310 0 1 75310 25999 99529 25171 42651 2835593044 25171 2835618215 0.000888 42651 142180 29.997890 0.798147554130 0.700021100014 0.999991123276 0.999976083311 0.850006111645 0.776381478361 0.745870803357 0.717667503101 0.594732030284 0.747475838282 0.747464055956 0.000023916689 0.745858895755 0.987774378220 0.965622291196 0.976572729149 0.976572729149
155 NORWEGIAN_BOKMAL_LUCENE_NORWEGIAN_LIGHT_STEM_FILTER NB_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 17914 75251 252 75251 0 1 75251 25985 99450 25118 42641 2831151666 25118 2831176784 0.000887 42641 142091 30.009642 0.798359129150 0.699903582915 0.999991128071 0.999976068044 0.849947355493 0.776512696705 0.745896444523 0.717602881668 0.594764635875 0.747512150366 0.747500361706 0.000023931956 0.745884529658 0.987789184092 0.965602788874 0.976569991255 0.976569991255
156 NORWEGIAN_BOKMAL_LUCENE_NORWEGIAN_MINIMAL_STEM_FILTER NB_NO ALL_WORDS PRIMARY_OUTPUT 17929 75310 252 75310 0 1 75310 27457 94526 14772 47654 2835603443 14772 2835618215 0.000521 47654 142180 33.516669 0.864846566268 0.664833309889 0.999994790554 0.999977986151 0.832414050221 0.815762584315 0.751763573752 0.697075888841 0.602260563739 0.758273568838 0.758263230800 0.000022013849 0.751752754540 0.992088894987 0.962515968891 0.977078714611 0.977078714611
157 NORWEGIAN_BOKMAL_LUCENE_NORWEGIAN_MINIMAL_STEM_FILTER NB_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 17914 75251 252 75251 0 1 75251 27443 94447 14719 47644 2831162065 14719 2831176784 0.000520 47644 142091 33.530625 0.865168642251 0.664693752595 0.999994801102 0.999977973869 0.832344276848 0.815949754214 0.751795969864 0.696994967013 0.602302149098 0.758335144541 0.758324803793 0.000022026131 0.751785145440 0.992107474043 0.962494026490 0.977076419047 0.977076419047
158 NORWEGIAN_BOKMAL_RADIXOR NB_NO ALL_WORDS PRIMARY_OUTPUT 17929 75310 252 75310 0 1 75310 17886 135010 11482 7170 2835606733 11482 2835618215 0.000405 7170 142180 5.042903 0.921620293258 0.949570966381 0.999995950795 0.999993422575 0.974783458588 0.927078011613 0.935386875069 0.943846020481 0.878616704195 0.935491246621 0.935487968734 0.000006577425 0.935383586923 0.993354053349 0.994615320153 0.993984286646 0.993984286646
159 NORWEGIAN_BOKMAL_RADIXOR NB_NO ALL_WORDS ANY_CANDIDATE 17929 75310 252 71073 4237 9 79825 17962 142180 0 0 2835618215 0 2835618215 0.000000 0 142180 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
160 NORWEGIAN_BOKMAL_RADIXOR NB_NO ALL_WORDS ALL_CANDIDATES 17929 75310 252 71073 4237 9 79825 17962 142180 20161 0 2835598054 20161 2835618215 0.000711 0 142180 0.000000 0.875810793330 1.000000000000 0.999992890087 0.999992890443 0.999996445043 0.898118108406 0.933794385280 0.972422273928 0.875810793330 0.935847633608 0.935844306704 0.000007109557
161 NORWEGIAN_BOKMAL_RADIXOR NB_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 17914 75251 252 75251 0 1 75251 17838 134987 11482 7104 2831165302 11482 2831176784 0.000406 7104 142091 4.999613 0.921607985307 0.950003870759 0.999995944443 0.999993435568 0.974999907601 0.927150543912 0.935590518436 0.944185565020 0.878976122105 0.935698217036 0.935694946007 0.000006564432 0.935587236809 0.993348241255 0.994693619298 0.994020475044 0.994020475044
162 NORWEGIAN_BOKMAL_RADIXOR NB_NO LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 17914 75251 252 71047 4204 9 79733 17914 142091 0 0 2831176784 0 2831176784 0.000000 0 142091 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
163 NORWEGIAN_BOKMAL_RADIXOR NB_NO LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 17914 75251 252 71047 4204 9 79733 17914 142091 20161 0 2831156623 20161 2831176784 0.000712 0 142091 0.000000 0.875742671893 1.000000000000 0.999992878933 0.999992879290 0.999996439466 0.898060798964 0.933755663840 0.972405477022 0.875742671893 0.935811237319 0.935807905326 0.000007120710
164 PERSIAN_LUCENE_PERSIAN_STEM_FILTER FA_IR ALL_WORDS PRIMARY_OUTPUT 69 3701 0 3701 0 1 3701 3190 430 179 98018 6748223 179 6748402 0.002652 98018 98448 99.563221 0.706075533662 0.004367788071 0.999973475202 0.985658076342 0.502170631636 0.021311605408 0.008681870034 0.005451304637 0.004359860890 0.055533668104 0.054800599292 0.014341923658 0.008506575635 0.985685778881 0.520346904570 0.681125382676 0.681125382676
165 PERSIAN_LUCENE_PERSIAN_STEM_FILTER FA_IR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 69 3701 0 3701 0 1 3701 3190 430 179 98018 6748223 179 6748402 0.002652 98018 98448 99.563221 0.706075533662 0.004367788071 0.999973475202 0.985658076342 0.502170631636 0.021311605408 0.008681870034 0.005451304637 0.004359860890 0.055533668104 0.054800599292 0.014341923658 0.008506575635 0.985685778881 0.520346904570 0.681125382676 0.681125382676
166 PERSIAN_RADIXOR FA_IR ALL_WORDS PRIMARY_OUTPUT 69 3701 0 3701 0 1 3701 69 93636 8621 4812 6739781 8621 6748402 0.127749 4812 98448 4.887860 0.915692813206 0.951121404193 0.998722512381 0.998038075904 0.974921958287 0.922565796215 0.933070924989 0.943818050233 0.874538848780 0.933239001706 0.932248664283 0.001961924096 0.932075735269 0.980342969277 0.984586447427 0.982460126226 0.982460126226
167 PERSIAN_RADIXOR FA_IR ALL_WORDS ANY_CANDIDATE 69 3701 0 3387 314 2 4015 69 98448 0 0 6748402 0 6748402 0.000000 0 98448 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
168 PERSIAN_RADIXOR FA_IR ALL_WORDS ALL_CANDIDATES 69 3701 0 3387 314 2 4015 69 98448 13433 0 6734969 13433 6748402 0.199055 0 98448 0.000000 0.879934930864 1.000000000000 0.998009454683 0.998038075904 0.999004727341 0.901584696651 0.936133391021 0.973435401930 0.879934930864 0.938048469358 0.937114390300 0.001961924096
169 PERSIAN_RADIXOR FA_IR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 69 3701 0 3701 0 1 3701 69 93636 8621 4812 6739781 8621 6748402 0.127749 4812 98448 4.887860 0.915692813206 0.951121404193 0.998722512381 0.998038075904 0.974921958287 0.922565796215 0.933070924989 0.943818050233 0.874538848780 0.933239001706 0.932248664283 0.001961924096 0.932075735269 0.980342969277 0.984586447427 0.982460126226 0.982460126226
170 PERSIAN_RADIXOR FA_IR LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 69 3701 0 3387 314 2 4015 69 98448 0 0 6748402 0 6748402 0.000000 0 98448 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
171 PERSIAN_RADIXOR FA_IR LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 69 3701 0 3387 314 2 4015 69 98448 13433 0 6734969 13433 6748402 0.199055 0 98448 0.000000 0.879934930864 1.000000000000 0.998009454683 0.998038075904 0.999004727341 0.901584696651 0.936133391021 0.973435401930 0.879934930864 0.938048469358 0.937114390300 0.001961924096
172 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL ALL_WORDS PRIMARY_OUTPUT 9990 122341 1 122341 0 1 122341 15519 1004747 99228 116220 7482378775 99228 7482478003 0.001326 116220 1120967 10.367834 0.910117529835 0.896321657997 0.999986738618 0.999971210643 0.948154198307 0.907324485129 0.903166914014 0.899047271013 0.823431500703 0.903193253581 0.903178865015 0.000028789357 0.903152518035 0.990022261217 0.977053921984 0.983495343428 0.983495343428
173 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL ALL_WORDS ANY_CANDIDATE 9990 122341 1 109468 12873 5 136636 16295 1093112 85532 27855 7482392471 85532 7482478003 0.001143 27855 1120967 2.484908 0.927431862377 0.975150918805 0.999988569028 0.999984848600 0.987569743916 0.936598359399 0.950692965028 0.965218263555 0.906019814355 0.950992130738 0.950984648194 0.000015151400
174 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL ALL_WORDS ALL_CANDIDATES 9990 122341 1 109468 12873 5 136636 16295 1093112 143096 27855 7482334907 143096 7482478003 0.001912 27855 1120967 2.484908 0.884246016852 0.975150918805 0.999980875854 0.999977156579 0.987565897330 0.901045352805 0.927476322293 0.955504786999 0.864760696263 0.928586730350 0.928575670129 0.000022843421
175 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 9846 120925 1 120925 0 1 120925 15277 999138 99224 115513 7310153475 99224 7310252699 0.001357 115513 1114651 10.363154 0.909661841906 0.896368459724 0.999986426735 0.999970629707 0.948177443229 0.906971715650 0.902966227492 0.898995962905 0.823097930182 0.902990688822 0.902976009256 0.000029370293 0.902951540918 0.989889334726 0.977011745487 0.983408384376 0.983408384376
176 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 9846 120925 1 108162 12763 5 135105 16044 1087157 85532 27494 7310167167 85532 7310252699 0.001170 27494 1114651 2.466602 0.927063356099 0.975333983462 0.999988299720 0.999984541059 0.987661141591 0.936331423447 0.950586270515 0.965281863330 0.905826028197 0.950892420848 0.950884788442 0.000015458941
177 POLISH_LUCENE_MORFOLOGIK_FILTER PL_PL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 9846 120925 1 108162 12763 5 135105 16044 1087157 143085 27494 7310109614 143085 7310252699 0.001957 27494 1114651 2.466602 0.883693614752 0.975333983462 0.999980426805 0.999976669344 0.987657205134 0.900617649988 0.927255102898 0.955516285728 0.864376148890 0.928383764096 0.928372472930 0.000023330656
178 POLISH_LUCENE_STEMPEL_DIRECT PL_PL ALL_WORDS PRIMARY_OUTPUT 9990 122341 1 122341 0 1 122341 31432 797573 66669 323394 7482411334 66669 7482478003 0.000891 323394 1120967 28.849556 0.922858412343 0.711504442147 0.999991089984 0.999947877619 0.855747766065 0.871105640425 0.803515398127 0.745658746735 0.671563509358 0.810319603524 0.810295555502 0.000052122381 0.803489769425 0.991766794523 0.931068514076 0.960459620808 0.960459620808
179 POLISH_LUCENE_STEMPEL_DIRECT PL_PL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 9846 120925 1 120925 0 1 120925 30830 794493 66274 320158 7310186425 66274 7310252699 0.000907 320158 1114651 28.722712 0.923005877316 0.712772876892 0.999990934103 0.999947146412 0.856381905497 0.871590591697 0.804379630033 0.746792242917 0.672771767894 0.811106376847 0.811081975971 0.000052853588 0.804353636298 0.991711086900 0.931317880193 0.960566151650 0.960566151650
180 POLISH_LUCENE_STEMPEL_FILTER PL_PL ALL_WORDS PRIMARY_OUTPUT 9990 122341 1 122341 0 1 122341 31432 797573 66669 323394 7482411334 66669 7482478003 0.000891 323394 1120967 28.849556 0.922858412343 0.711504442147 0.999991089984 0.999947877619 0.855747766065 0.871105640425 0.803515398127 0.745658746735 0.671563509358 0.810319603524 0.810295555502 0.000052122381 0.803489769425 0.991766794523 0.931068514076 0.960459620808 0.960459620808
181 POLISH_LUCENE_STEMPEL_FILTER PL_PL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 9846 120925 1 120925 0 1 120925 30830 794493 66274 320158 7310186425 66274 7310252699 0.000907 320158 1114651 28.722712 0.923005877316 0.712772876892 0.999990934103 0.999947146412 0.856381905497 0.871590591697 0.804379630033 0.746792242917 0.672771767894 0.811106376847 0.811081975971 0.000052853588 0.804353636298 0.991711086900 0.931317880193 0.960566151650 0.960566151650
182 POLISH_RADIXOR PL_PL ALL_WORDS PRIMARY_OUTPUT 9990 122341 1 122341 0 1 122341 10074 1099420 13669 21547 7482464334 13669 7482478003 0.000183 21547 1120967 1.922180 0.987719760055 0.980778203105 0.999998173199 0.999995294243 0.990388188152 0.986323599045 0.984236742499 0.982158698021 0.968962733423 0.984242862020 0.984240510632 0.000004705757 0.984234389298 0.996967243455 0.996469409869 0.996718264498 0.996718264498
183 POLISH_RADIXOR PL_PL ALL_WORDS ANY_CANDIDATE 9990 122341 1 119475 2866 4 125778 10079 1120967 0 0 7482478003 0 7482478003 0.000000 0 1120967 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
184 POLISH_RADIXOR PL_PL ALL_WORDS ALL_CANDIDATES 9990 122341 1 119475 2866 4 125778 10079 1120967 38073 0 7482439930 38073 7482478003 0.000509 0 1120967 0.000000 0.967151263114 1.000000000000 0.999994911712 0.999994912475 0.999997455856 0.973547222425 0.983301367057 0.993252946885 0.967151263114 0.983438489746 0.983435987734 0.000005087525
185 POLISH_RADIXOR PL_PL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 9846 120925 1 120925 0 1 120925 9844 1093651 13669 21000 7310239030 13669 7310252699 0.000187 21000 1114651 1.883998 0.987655781527 0.981160022285 0.999998130160 0.999995258206 0.990579076223 0.986349757961 0.984397186102 0.982452329568 0.969273787578 0.984402543989 0.984400174373 0.000004741794 0.984394814870 0.996926141446 0.996646530259 0.996786316244 0.996786316244
186 POLISH_RADIXOR PL_PL LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 9846 120925 1 118145 2780 4 124274 9847 1114651 0 0 7310252699 0 7310252699 0.000000 0 1114651 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
187 POLISH_RADIXOR PL_PL LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 9846 120925 1 118145 2780 4 124274 9847 1114651 38073 0 7310214626 38073 7310252699 0.000521 0 1114651 0.000000 0.966971278467 1.000000000000 0.999994791835 0.999994792629 0.999997395918 0.973401318686 0.983208335630 0.993214975136 0.966971278467 0.983346977657 0.983344416937 0.000005207371
188 PORTUGUESE_LUCENE_PORTUGUESE_LIGHT_STEM_FILTER PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 112814 149830 2511 5339230 22358201245 2511 22358203756 0.000011 5339230 5489060 97.270389 0.983517240927 0.027296112631 0.999999887692 0.999761142266 0.513648000162 0.122843213263 0.053118010934 0.033885033146 0.027283631587 0.163848092400 0.163827867245 0.000238857734 0.053105458848 0.999226179883 0.720580123442 0.837329788424 0.837329788424
189 PORTUGUESE_LUCENE_PORTUGUESE_LIGHT_STEM_FILTER PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 112814 149830 2511 5339230 22358201245 2511 22358203756 0.000011 5339230 5489060 97.270389 0.983517240927 0.027296112631 0.999999887692 0.999761142266 0.513648000162 0.122843213263 0.053118010934 0.033885033146 0.027283631587 0.163848092400 0.163827867245 0.000238857734 0.053105458848 0.999226179883 0.720580123442 0.837329788424 0.837329788424
190 PORTUGUESE_LUCENE_PORTUGUESE_MINIMAL_STEM_FILTER PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 167745 43406 598 5445654 22358203158 598 22358203756 0.000003 5445654 5489060 99.209227 0.986410326334 0.007907729192 0.999999973254 0.999756469021 0.503953851223 0.038310165654 0.015689679353 0.009864890589 0.007906867787 0.088319113068 0.088308061848 0.000243530979 0.015685836581 0.999664059174 0.692382565086 0.818121623354 0.818121623354
191 PORTUGUESE_LUCENE_PORTUGUESE_MINIMAL_STEM_FILTER PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 167745 43406 598 5445654 22358203158 598 22358203756 0.000003 5445654 5489060 99.209227 0.986410326334 0.007907729192 0.999999973254 0.999756469021 0.503953851223 0.038310165654 0.015689679353 0.009864890589 0.007906867787 0.088319113068 0.088308061848 0.000243530979 0.015685836581 0.999664059174 0.692382565086 0.818121623354 0.818121623354
192 PORTUGUESE_LUCENE_PORTUGUESE_STEM_FILTER PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 27586 3803488 99075 1685572 22358104681 99075 22358203756 0.000443 1685572 5489060 30.707844 0.974612837768 0.692921556696 0.999995568741 0.999920198913 0.846458562719 0.901329863268 0.809974591186 0.735433886866 0.680636384053 0.821784792219 0.821750382444 0.000079801087 0.809935821347 0.996728545383 0.918475110538 0.956003146785 0.956003146785
193 PORTUGUESE_LUCENE_PORTUGUESE_STEM_FILTER PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 27586 3803488 99075 1685572 22358104681 99075 22358203756 0.000443 1685572 5489060 30.707844 0.974612837768 0.692921556696 0.999995568741 0.999920198913 0.846458562719 0.901329863268 0.809974591186 0.735433886866 0.680636384053 0.821784792219 0.821750382444 0.000079801087 0.809935821347 0.996728545383 0.918475110538 0.956003146785 0.956003146785
194 PORTUGUESE_RADIXOR PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 4001 5472616 20678 16444 22358183078 20678 22358203756 0.000092 16444 5489060 0.299578 0.996235774018 0.997004222945 0.999999075149 0.999998340077 0.998501649047 0.996389369023 0.996619850353 0.996850438335 0.993262474550 0.996619924417 0.996619094289 0.000001659923 0.996619020188 0.999299376330 0.999346803887 0.999323089546 0.999323089546
195 PORTUGUESE_RADIXOR PT_PT ALL_WORDS ANY_CANDIDATE 4001 211489 0 210699 790 3 212297 4001 5489060 0 0 22358203756 0 22358203756 0.000000 0 5489060 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
196 PORTUGUESE_RADIXOR PT_PT ALL_WORDS ALL_CANDIDATES 4001 211489 0 210699 790 3 212297 4001 5489060 38310 0 22358165446 38310 22358203756 0.000171 0 5489060 0.000000 0.993069036450 1.000000000000 0.999998286535 0.999998286956 0.999999143267 0.994447532369 0.996522466897 0.998606078314 0.993069036450 0.996528492543 0.996527638784 0.000001713044
197 PORTUGUESE_RADIXOR PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 4001 5472616 20678 16444 22358183078 20678 22358203756 0.000092 16444 5489060 0.299578 0.996235774018 0.997004222945 0.999999075149 0.999998340077 0.998501649047 0.996389369023 0.996619850353 0.996850438335 0.993262474550 0.996619924417 0.996619094289 0.000001659923 0.996619020188 0.999299376330 0.999346803887 0.999323089546 0.999323089546
198 PORTUGUESE_RADIXOR PT_PT LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 4001 211489 0 210699 790 3 212297 4001 5489060 0 0 22358203756 0 22358203756 0.000000 0 5489060 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
199 PORTUGUESE_RADIXOR PT_PT LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 4001 211489 0 210699 790 3 212297 4001 5489060 38310 0 22358165446 38310 22358203756 0.000171 0 5489060 0.000000 0.993069036450 1.000000000000 0.999998286535 0.999998286956 0.999999143267 0.994447532369 0.996522466897 0.998606078314 0.993069036450 0.996528492543 0.996527638784 0.000001713044
200 RUSSIAN_LUCENE_RUSSIAN_LIGHT_STEM_FILTER RU_RU ALL_WORDS PRIMARY_OUTPUT 37410 768882 10 768882 0 1 768882 232250 3081067 321183 10008438 295575969833 321183 295576291016 0.000109 10008438 13089505 76.461547 0.905596884415 0.235384531348 0.999998913367 0.999965054154 0.617691722357 0.577011147253 0.373649378129 0.276277984307 0.229747124085 0.461696326851 0.461686629842 0.000034945846 0.373637933830 0.994310930069 0.870888421754 0.928516167212 0.928516167212
201 RUSSIAN_LUCENE_RUSSIAN_LIGHT_STEM_FILTER RU_RU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 37297 768133 10 768133 0 1 768133 232143 3078888 318921 10008238 295000362731 318921 295000681652 0.000108 10008238 13087126 76.473918 0.906139220892 0.235260820443 0.999998918914 0.999964994315 0.617629869679 0.577038425373 0.373539598427 0.276151716079 0.229664120975 0.461713175622 0.461703471649 0.000035005685 0.373528142103 0.994349544377 0.870767393403 0.928464208705 0.928464208705
202 RUSSIAN_RADIXOR RU_RU ALL_WORDS PRIMARY_OUTPUT 37410 768882 10 768882 0 1 768882 37561 12823203 155850 266302 295576135166 155850 295576291016 0.000053 266302 13089505 2.034470 0.987992190185 0.979655304001 0.999999472725 0.999998571830 0.989827388363 0.986313480705 0.983806085477 0.981311406466 0.968128298562 0.983814916245 0.983814202914 0.000001428170 0.983805371373 0.997699288696 0.997273959852 0.997486578934 0.997486578934
203 RUSSIAN_RADIXOR RU_RU ALL_WORDS ANY_CANDIDATE 37410 768882 10 749720 19162 4 788492 37593 13089492 0 13 295576291016 0 295576291016 0.000000 13 13089505 0.000099 1.000000000000 0.999999006838 1.000000000000 0.999999999956 0.999999503419 0.999999801367 0.999999503419 0.999999205470 0.999999006838 0.999999503419 0.999999503397 0.000000000044
204 RUSSIAN_RADIXOR RU_RU ALL_WORDS ALL_CANDIDATES 37410 768882 10 749720 19162 4 788492 37593 13089492 434710 13 295575856306 434710 295576291016 0.000147 13 13089505 0.000099 0.967856883534 0.999999006838 0.999998529280 0.999998529301 0.999998768059 0.974118939969 0.983665447282 0.993400920813 0.967855953192 0.983796687479 0.983795964011 0.000001470699
205 RUSSIAN_RADIXOR RU_RU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 37297 768133 10 768133 0 1 768133 37282 12821513 155850 265613 295000525802 155850 295000681652 0.000053 265613 13087126 2.029575 0.987990626447 0.979704252867 0.999999471696 0.999998571379 0.989851862281 0.986322156837 0.983829991833 0.981350389119 0.968174600634 0.983838715706 0.983838002141 0.000001428621 0.983829277503 0.997696524283 0.997321167437 0.997508810549 0.997508810549
206 RUSSIAN_RADIXOR RU_RU LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 37297 768133 10 749142 18991 4 787549 37306 13087126 0 0 295000681652 0 295000681652 0.000000 0 13087126 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
207 RUSSIAN_RADIXOR RU_RU LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 37297 768133 10 749142 18991 4 787549 37306 13087126 434710 0 295000246942 434710 295000681652 0.000147 0 13087126 0.000000 0.967851259252 1.000000000000 0.999998526410 0.999998526476 0.999999263205 0.974114570610 0.983663023007 0.993400519870 0.967851259252 0.983794317554 0.983793592699 0.000001473524
208 SNOWBALL_DANISH_DIRECT DA_DK ALL_WORDS PRIMARY_OUTPUT 4179 28079 32 28079 0 1 28079 5553 78732 6341 11163 394104845 6341 394111186 0.001609 11163 89895 12.417821 0.925464013259 0.875821792091 0.999983910632 0.999955596266 0.937902851361 0.915090414169 0.899958849618 0.885319563795 0.818113803566 0.900300811178 0.900278764621 0.000044403734 0.899936659693 0.994195946109 0.978579164615 0.986325742978 0.986325742978
209 SNOWBALL_DANISH_DIRECT DA_DK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4173 28033 32 28033 0 1 28033 5539 78627 6341 11113 392814447 6341 392820788 0.001614 11113 89740 12.383552 0.925371904717 0.876164475150 0.999983857779 0.999955577673 0.938074166465 0.915093153823 0.900096160451 0.885582797210 0.818340774971 0.900432112497 0.900410054086 0.000044422327 0.900073960926 0.994185354918 0.978644331817 0.986353630937 0.986353630937
210 SNOWBALL_DANISH_LUCENE_FILTER DA_DK ALL_WORDS PRIMARY_OUTPUT 4179 28079 32 28079 0 1 28079 5546 78744 6507 11151 394104679 6507 394111186 0.001651 11151 89895 12.404472 0.923672449590 0.875955281161 0.999983489431 0.999955205602 0.937969385296 0.913717599716 0.899181254496 0.885100184115 0.816829526358 0.899497504322 0.899475250557 0.000044794398 0.899158868074 0.994052746860 0.978603476314 0.986267614487 0.986267614487
211 SNOWBALL_DANISH_LUCENE_FILTER DA_DK LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4173 28033 32 28033 0 1 28033 5539 78627 6341 11113 392814447 6341 392820788 0.001614 11113 89740 12.383552 0.925371904717 0.876164475150 0.999983857779 0.999955577673 0.938074166465 0.915093153823 0.900096160451 0.885582797210 0.818340774971 0.900432112497 0.900410054086 0.000044422327 0.900073960926 0.994185354918 0.978644331817 0.986353630937 0.986353630937
212 SNOWBALL_DUTCH_DIRECT NL_NL ALL_WORDS PRIMARY_OUTPUT 4992 26477 85 26477 0 1 26477 12051 29325 4382 35241 350433578 4382 350437960 0.001250 35241 64566 54.581359 0.869997329931 0.454186413902 0.999987495647 0.999886953739 0.727086954774 0.735353119953 0.596806854375 0.502190286022 0.425320531415 0.628602392126 0.628557411604 0.000113046261 0.596755898222 0.992814719235 0.917346281080 0.953589661124 0.953589661124
213 SNOWBALL_DUTCH_DIRECT NL_NL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4796 25678 84 25678 0 1 25678 11466 29111 4382 34036 329599474 4382 329603856 0.001329 34036 63147 53.899631 0.869166691547 0.461003689803 0.999986705253 0.999883464224 0.730495197528 0.738411822300 0.602462748344 0.508789468717 0.431088865524 0.633000040962 0.632953313739 0.000116535776 0.602409959779 0.992557044900 0.918058993802 0.953855618789 0.953855618789
214 SNOWBALL_DUTCH_LUCENE_FILTER NL_NL ALL_WORDS PRIMARY_OUTPUT 4992 26477 85 26477 0 1 26477 14573 15302 1588 49264 350436372 1588 350437960 0.000453 49264 64566 76.300220 0.905979869745 0.236997800700 0.999995468527 0.999854916880 0.618496634614 0.579068464950 0.375712040856 0.278062466837 0.231308764398 0.463373754768 0.463333378452 0.000145083120 0.375664346452 0.995827666179 0.888410302915 0.939057139355 0.939057139355
215 SNOWBALL_DUTCH_LUCENE_FILTER NL_NL LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4796 25678 84 25678 0 1 25678 14116 14972 1544 48175 329602312 1544 329603856 0.000468 48175 63147 76.290243 0.906514894648 0.237097565997 0.999995315589 0.999849184178 0.618546440793 0.579362438183 0.375883408860 0.278182412747 0.231438685443 0.463608105042 0.463566154833 0.000150815822 0.375833834664 0.995816517119 0.887491664267 0.938538755173 0.938538755173
216 SNOWBALL_FINNISH_DIRECT FI_FI ALL_WORDS PRIMARY_OUTPUT 57027 1811717 292 1811717 0 1 1811717 381483 15114332 1544812 16409363 1641125269679 1544812 1641126814491 0.000094 16409363 31523695 52.054060 0.907269425128 0.479459403474 0.999999058688 0.999989060059 0.739729231081 0.769880311353 0.627374073993 0.529384116965 0.457061215373 0.659544431681 0.659540149918 0.000010939941 0.627369124557 0.991871857177 0.904138579582 0.945975396220 0.945975396220
217 SNOWBALL_FINNISH_DIRECT FI_FI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 54762 1757055 274 1757055 0 1 1757055 372232 14692070 1513705 16121763 1543587930447 1513705 1543589444152 0.000098 16121763 30813833 52.319888 0.906594717007 0.476801117213 0.999999019360 0.999988575255 0.738400068286 0.768116957494 0.624933751043 0.526744348874 0.454475376380 0.657468914800 0.657464451566 0.000011424745 0.624928589970 0.991731678095 0.902933406396 0.945251664438 0.945251664438
218 SNOWBALL_FINNISH_LUCENE_FILTER FI_FI ALL_WORDS PRIMARY_OUTPUT 57027 1811717 292 1811717 0 1 1811717 377778 15153638 1922153 16370057 1641124892338 1922153 1641126814491 0.000117 16370057 31523695 51.929372 0.887434028678 0.480706275073 0.999998828760 0.999988854086 0.740352551917 0.758996033322 0.623613097472 0.529216231176 0.453079796332 0.653142485450 0.653138019077 0.000011145914 0.623608016975 0.990717710840 0.904385055188 0.945584912497 0.945584912497
219 SNOWBALL_FINNISH_LUCENE_FILTER FI_FI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 54762 1757055 274 1757055 0 1 1757055 372232 14692070 1513705 16121763 1543587930447 1513705 1543589444152 0.000098 16121763 30813833 52.319888 0.906594717007 0.476801117213 0.999999019360 0.999988575255 0.738400068286 0.768116957494 0.624933751043 0.526744348874 0.454475376380 0.657468914800 0.657464451566 0.000011424745 0.624928589970 0.991731678095 0.902933406396 0.945251664438 0.945251664438
220 SNOWBALL_FRENCH_DIRECT FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 85627 3766640 1654723 1687975 90394450107 1654723 90396104830 0.001831 1687975 5454615 30.945814 0.694777309691 0.690541862258 0.999981694753 0.999963023890 0.845261778506 0.693926068790 0.692653111288 0.691384815533 0.529815856272 0.692656348624 0.692637859933 0.000036976110 0.692634622294 0.959459328254 0.944947915186 0.952148333884 0.952148333884
221 SNOWBALL_FRENCH_DIRECT FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 84526 3758589 1646111 1681970 88710480395 1646111 88712126506 0.001856 1681970 5440559 30.915389 0.695429718578 0.690846106071 0.999981444352 0.999962486787 0.845413775212 0.694508136723 0.693130334647 0.691757988461 0.530374491828 0.693134123475 0.693115366288 0.000037513213 0.693111577099 0.959520798119 0.944537159644 0.951970023370 0.951970023370
222 SNOWBALL_FRENCH_LUCENE_FILTER FR_FR ALL_WORDS PRIMARY_OUTPUT 59240 425210 2301 425210 0 1 425210 85202 3763777 1661388 1690838 90394443442 1661388 90396104830 0.001838 1690838 5454615 30.998301 0.693762678186 0.690016985617 0.999981621022 0.999962918494 0.844999303320 0.693010289898 0.691884762376 0.690762884895 0.528917286853 0.691887297134 0.691868755638 0.000037081506 0.691866220643 0.958697792387 0.944714715363 0.951654891797 0.951654891797
223 SNOWBALL_FRENCH_LUCENE_FILTER FR_FR LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 57698 421231 2133 421231 0 1 421231 84810 3755856 1641925 1684703 88710484581 1641925 88712126506 0.001851 1684703 5440559 30.965623 0.695814817237 0.690343767984 0.999981491538 0.999962503165 0.845162629761 0.694713680979 0.693068495729 0.691431084156 0.530302080457 0.693073894149 0.693055145391 0.000037496835 0.693049746458 0.959566165512 0.944384738154 0.951914926182 0.951914926182
224 SNOWBALL_GERMAN_DIRECT DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 81649 771138 190680 612734 44095055299 190680 44095245979 0.000432 612734 1383872 44.276783 0.801750435114 0.557232171762 0.999995675724 0.999981780603 0.778613923743 0.737064397386 0.657493530688 0.593429030432 0.489750735447 0.668401927114 0.668393541401 0.000018219397 0.657484715679 0.983724573695 0.949324273697 0.966218331938 0.966218331938
225 SNOWBALL_GERMAN_DIRECT DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 37843 516936 87697 356475 11263668645 87697 11263756342 0.000779 356475 873411 40.814118 0.854958297017 0.591858815609 0.999992214231 0.999960569321 0.795925514920 0.785153327381 0.699486618802 0.630674793334 0.537854226580 0.711347035607 0.711329191110 0.000039430679 0.699467554207 0.988417636496 0.932451723900 0.959619376607 0.959619376607
226 SNOWBALL_GERMAN_LUCENE_FILTER DE_DE ALL_WORDS PRIMARY_OUTPUT 54092 296974 1474 296974 0 1 296974 86669 751056 295701 632816 44094950278 295701 44095245979 0.000671 632816 1383872 45.727929 0.717507501741 0.542720714054 0.999993294039 0.999978943584 0.771357004047 0.674088567377 0.617993120299 0.570516594262 0.447170798768 0.624024185176 0.624014089276 0.000021056416 0.617982794334 0.975844522648 0.942925157079 0.959102449371 0.959102449371
227 SNOWBALL_GERMAN_LUCENE_FILTER DE_DE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 16007 150098 228 150098 0 1 150098 46077 481501 77653 391910 11263678689 77653 11263756342 0.000689 391910 873411 44.871200 0.861124126806 0.551287996144 0.999993105941 0.999958315274 0.775640551042 0.774110642769 0.672222202832 0.594035281304 0.506276128631 0.689004640259 0.688986412764 0.000041684726 0.672202362239 0.989021274644 0.919542244735 0.953017108147 0.953017108147
228 SNOWBALL_HUNGARIAN_DIRECT HU_HU ALL_WORDS PRIMARY_OUTPUT 19406 916344 1 916344 0 1 916344 116105 14287912 1506056 7874191 419819036837 1506056 419820542893 0.000359 7874191 22162103 35.529981 0.904643595580 0.644700189328 0.999996412620 0.999977657711 0.822348300974 0.837136808086 0.752865700984 0.684009307333 0.603676525918 0.763690969794 0.763680928295 0.000022342289 0.752854843818 0.991947513126 0.924304257644 0.956931989551 0.956931989551
229 SNOWBALL_HUNGARIAN_DIRECT HU_HU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 18360 878513 1 878513 0 1 878513 111379 13776526 1496670 7634885 385869198247 1496670 385870694917 0.000388 7634885 21411411 35.658019 0.902006757459 0.643419810119 0.999996121317 0.999976336507 0.821707965718 0.834898516372 0.751079383241 0.682554714263 0.601382804609 0.761819543337 0.761808891723 0.000023663493 0.751067882221 0.991609896137 0.923288102987 0.956230170303 0.956230170303
230 SNOWBALL_HUNGARIAN_LUCENE_FILTER HU_HU ALL_WORDS PRIMARY_OUTPUT 19406 916344 1 916344 0 1 916344 114867 14299358 1792049 7862745 419818750844 1792049 419820542893 0.000427 7862745 22162103 35.478334 0.888633169244 0.645216656560 0.999995731393 0.999977003783 0.822606193976 0.826287586346 0.747610245439 0.682613266689 0.596946950992 0.757205997314 0.757195513225 0.000022996217 0.747599036407 0.990687085622 0.924490230693 0.956444632598 0.956444632598
231 SNOWBALL_HUNGARIAN_LUCENE_FILTER HU_HU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 18360 878513 1 878513 0 1 878513 111379 13776526 1496670 7634885 385869198247 1496670 385870694917 0.000388 7634885 21411411 35.658019 0.902006757459 0.643419810119 0.999996121317 0.999976336507 0.821707965718 0.834898516372 0.751079383241 0.682554714263 0.601382804609 0.761819543337 0.761808891723 0.000023663493 0.751067882221 0.991609896137 0.923288102987 0.956230170303 0.956230170303
232 SNOWBALL_ITALIAN_DIRECT IT_IT ALL_WORDS PRIMARY_OUTPUT 10009 327551 0 327551 0 1 327551 46828 4499650 504775 1644164 53638016436 504775 53638521211 0.000941 1644164 6143814 26.761292 0.899134266174 0.732387080729 0.999990589319 0.999959941236 0.866188835024 0.859975076366 0.807239600802 0.760598128154 0.676782697802 0.811488952720 0.811469907061 0.000040058764 0.807219778599 0.987993752409 0.933407841859 0.959925420024 0.959925420024
233 SNOWBALL_ITALIAN_DIRECT IT_IT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 10007 327469 0 327469 0 1 327469 46814 4498652 504774 1643522 53611162298 504774 53611667072 0.000942 1643522 6142174 26.757985 0.899114326863 0.732420149608 0.999990584624 0.999959933163 0.866205367116 0.859969602244 0.807251650876 0.760623806435 0.676799637969 0.811498274672 0.811479224597 0.000040066837 0.807231824542 0.987990915845 0.933413003102 0.959926810498 0.959926810498
234 SNOWBALL_ITALIAN_LUCENE_FILTER IT_IT ALL_WORDS PRIMARY_OUTPUT 10009 327551 0 327551 0 1 327551 46828 4499650 504775 1644164 53638016436 504775 53638521211 0.000941 1644164 6143814 26.761292 0.899134266174 0.732387080729 0.999990589319 0.999959941236 0.866188835024 0.859975076366 0.807239600802 0.760598128154 0.676782697802 0.811488952720 0.811469907061 0.000040058764 0.807219778599 0.987993752409 0.933407841859 0.959925420024 0.959925420024
235 SNOWBALL_ITALIAN_LUCENE_FILTER IT_IT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 10007 327469 0 327469 0 1 327469 46814 4498652 504774 1643522 53611162298 504774 53611667072 0.000942 1643522 6142174 26.757985 0.899114326863 0.732420149608 0.999990584624 0.999959933163 0.866205367116 0.859969602244 0.807251650876 0.760623806435 0.676799637969 0.811498274672 0.811479224597 0.000040066837 0.807231824542 0.987990915845 0.933413003102 0.959926810498 0.959926810498
236 SNOWBALL_NORWEGIAN_BOKMAL_DIRECT NB_NO ALL_WORDS PRIMARY_OUTPUT 17929 75310 252 75310 0 1 75310 24394 106626 23997 35554 2835594218 23997 2835618215 0.000846 35554 142180 25.006330 0.816288096277 0.749936699958 0.999991537295 0.999978999989 0.874964118626 0.802094867845 0.781706946038 0.762329786671 0.641641141674 0.782409356499 0.782398932962 0.000021000011 0.781696464373 0.988328173631 0.971119668400 0.979648355684 0.979648355684
237 SNOWBALL_NORWEGIAN_BOKMAL_DIRECT NB_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 17914 75251 252 75251 0 1 75251 24367 106567 23997 35524 2831152787 23997 2831176784 0.000848 35524 142091 25.000880 0.816205079501 0.749991202821 0.999991524019 0.999978977642 0.874991363420 0.802043209347 0.781698483431 0.762360357576 0.641629738452 0.782397999310 0.782387564360 0.000021022358 0.781687990535 0.988317966243 0.971127035574 0.979647089734 0.979647089734
238 SNOWBALL_NORWEGIAN_BOKMAL_LUCENE_FILTER NB_NO ALL_WORDS PRIMARY_OUTPUT 17929 75310 252 75310 0 1 75310 24396 106589 24046 35591 2835594169 24046 2835618215 0.000848 35591 142180 25.032353 0.815929880966 0.749676466451 0.999991520015 0.999978969662 0.874833993233 0.801758635215 0.781401315910 0.762052176648 0.641229410562 0.782101930719 0.782091491842 0.000021030338 0.781390819068 0.988295184140 0.971086244692 0.979615142710 0.979615142710
239 SNOWBALL_NORWEGIAN_BOKMAL_LUCENE_FILTER NB_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 17914 75251 252 75251 0 1 75251 24381 106512 23993 35579 2831152791 23993 2831176784 0.000847 35579 142091 25.039587 0.816152637830 0.749604126933 0.999991525432 0.999978959629 0.874797826182 0.801914137847 0.781464144742 0.762031224736 0.641314033862 0.782170943927 0.782160500766 0.000021040371 0.781453643056 0.988310445474 0.971073741466 0.979616277822 0.979616277822
240 SNOWBALL_NORWEGIAN_NYNORSK_DIRECT NN_NO ALL_WORDS PRIMARY_OUTPUT 4688 18250 23 18250 0 1 18250 6138 22004 8274 8648 166483199 8274 166491473 0.004970 8648 30652 28.213493 0.726732280864 0.717865065901 0.999950303761 0.999898379870 0.858907684831 0.724941356316 0.722271459051 0.719621155632 0.565277706417 0.722285066089 0.722234252664 0.000101620130 0.722220641604 0.980542486408 0.964998187466 0.972708239744 0.972708239744
241 SNOWBALL_NORWEGIAN_NYNORSK_DIRECT NN_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4681 18219 23 18219 0 1 18219 6120 21971 8274 8624 165918002 8274 165926276 0.004987 8624 30595 28.187612 0.726434121342 0.718123876450 0.999950134480 0.999898178365 0.859037005465 0.724756721095 0.722255095332 0.719770679771 0.565257660346 0.722267047015 0.722216132089 0.000101821635 0.722204176866 0.980505813025 0.965064509051 0.972723884952 0.972723884952
242 SNOWBALL_NORWEGIAN_NYNORSK_LUCENE_FILTER NN_NO ALL_WORDS PRIMARY_OUTPUT 4688 18250 23 18250 0 1 18250 6144 21978 8295 8674 166483178 8295 166491473 0.004982 8674 30652 28.298317 0.725993459518 0.717016834138 0.999950177629 0.999898097625 0.858483505883 0.724180198229 0.721477226098 0.718794356395 0.564305338023 0.721491186328 0.721440231913 0.000101902375 0.721426267560 0.980461058483 0.964862123312 0.972599049418 0.972599049418
243 SNOWBALL_NORWEGIAN_NYNORSK_LUCENE_FILTER NN_NO LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4681 18219 23 18219 0 1 18219 6130 21948 8274 8647 165918002 8274 165926276 0.004987 8647 30595 28.262788 0.726225928132 0.717372119627 0.999950134480 0.999898039774 0.858661127054 0.724437725685 0.721771872996 0.719125568472 0.564665929147 0.721785448310 0.721734464790 0.000101960226 0.721720885459 0.980505813025 0.964944952705 0.972663150405 0.972663150405
244 SNOWBALL_PORTUGUESE_DIRECT PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 11315 4817239 167230 671821 22358036526 167230 22358203756 0.000748 671821 5489060 12.239272 0.966449786326 0.877607277020 0.999992520419 0.999962481554 0.938799898719 0.947270839082 0.919888415834 0.894044585092 0.851660540743 0.920957852105 0.920939611009 0.000037518446 0.919869695779 0.996663145176 0.967923515462 0.982083116554 0.982083116554
245 SNOWBALL_PORTUGUESE_DIRECT PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 11315 4817239 167230 671821 22358036526 167230 22358203756 0.000748 671821 5489060 12.239272 0.966449786326 0.877607277020 0.999992520419 0.999962481554 0.938799898719 0.947270839082 0.919888415834 0.894044585092 0.851660540743 0.920957852105 0.920939611009 0.000037518446 0.919869695779 0.996663145176 0.967923515462 0.982083116554 0.982083116554
246 SNOWBALL_PORTUGUESE_LUCENE_FILTER PT_PT ALL_WORDS PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 11315 4817239 167230 671821 22358036526 167230 22358203756 0.000748 671821 5489060 12.239272 0.966449786326 0.877607277020 0.999992520419 0.999962481554 0.938799898719 0.947270839082 0.919888415834 0.894044585092 0.851660540743 0.920957852105 0.920939611009 0.000037518446 0.919869695779 0.996663145176 0.967923515462 0.982083116554 0.982083116554
247 SNOWBALL_PORTUGUESE_LUCENE_FILTER PT_PT LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 4001 211489 0 211489 0 1 211489 11315 4817239 167230 671821 22358036526 167230 22358203756 0.000748 671821 5489060 12.239272 0.966449786326 0.877607277020 0.999992520419 0.999962481554 0.938799898719 0.947270839082 0.919888415834 0.894044585092 0.851660540743 0.920957852105 0.920939611009 0.000037518446 0.919869695779 0.996663145176 0.967923515462 0.982083116554 0.982083116554
248 SNOWBALL_RUSSIAN_DIRECT RU_RU ALL_WORDS PRIMARY_OUTPUT 37410 768882 10 768882 0 1 768882 64358 8766656 3782908 4322849 295572508108 3782908 295576291016 0.001280 4322849 13089505 33.025305 0.698562595481 0.669746946122 0.999987201585 0.999972577645 0.834867073854 0.692602792505 0.683851352013 0.675318311031 0.519585195076 0.684003044583 0.683989349009 0.000027422355 0.683837646322 0.974179960240 0.953661001039 0.963811283954 0.963811283954
249 SNOWBALL_RUSSIAN_DIRECT RU_RU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 37297 768133 10 768133 0 1 768133 64159 8764719 3782908 4322407 294996898744 3782908 295000681652 0.001282 4322407 13087126 33.027931 0.698516062041 0.669720685810 0.999987176613 0.999972525638 0.834853931211 0.692560581516 0.683815365804 0.675288254087 0.519543647630 0.683966853085 0.683953131513 0.000027474362 0.683801634112 0.974148936252 0.953633561396 0.963782086975 0.963782086975
250 SNOWBALL_RUSSIAN_LUCENE_FILTER RU_RU ALL_WORDS PRIMARY_OUTPUT 37410 768882 10 768882 0 1 768882 64266 8766889 3785790 4322616 295572505226 3785790 295576291016 0.001281 4322616 13089505 33.023525 0.698407806015 0.669764746642 0.999987191835 0.999972568683 0.834875969239 0.692484865100 0.683786451263 0.675303850911 0.519510266339 0.683936347366 0.683922647122 0.000027431317 0.683772741022 0.974131099393 0.953673855106 0.963793934411 0.963793934411
251 SNOWBALL_RUSSIAN_LUCENE_FILTER RU_RU LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 37297 768133 10 768133 0 1 768133 64159 8764719 3782908 4322407 294996898744 3782908 295000681652 0.001282 4322407 13087126 33.027931 0.698516062041 0.669720685810 0.999987176613 0.999972525638 0.834853931211 0.692560581516 0.683815365804 0.675288254087 0.519543647630 0.683966853085 0.683953131513 0.000027474362 0.683801634112 0.974148936252 0.953633561396 0.963782086975 0.963782086975
252 SNOWBALL_SPANISH_DIRECT ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 195021 12811687 2228819 29161649 379565089291 2228819 379567318110 0.000587 29161649 41973336 69.476605 0.851812232913 0.305233946618 0.999994128001 0.999917308483 0.652614037309 0.627191552465 0.449423738186 0.350172671706 0.289843040458 0.509903921959 0.509876023351 0.000082691517 0.449391616998 0.981405614580 0.852462513401 0.912400934512 0.912400934512
253 SNOWBALL_SPANISH_DIRECT ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 194444 12787018 2201196 29076352 377858468569 2201196 377860669765 0.000583 29076352 41863370 69.455354 0.853138205793 0.305446455935 0.999994174583 0.999917233823 0.652720315259 0.627945981812 0.449838583213 0.350441220963 0.290188220622 0.510478247707 0.510450359519 0.000082766177 0.449806446076 0.981468761133 0.852555702466 0.912481600659 0.912481600659
254 SNOWBALL_SPANISH_LUCENE_FILTER ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 194971 12811693 2230481 29161643 379565087629 2230481 379567318110 0.000588 29161643 41973336 69.476591 0.851718175843 0.305234089566 0.999994123622 0.999917304121 0.652614106594 0.627150877515 0.449410800675 0.350169642836 0.289832278511 0.509875888791 0.509847985527 0.000082695879 0.449378676109 0.981386049614 0.852460744495 0.912391466047 0.912391466047
255 SNOWBALL_SPANISH_LUCENE_FILTER ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 194444 12787018 2201196 29076352 377858468569 2201196 377860669765 0.000583 29076352 41863370 69.455354 0.853138205793 0.305446455935 0.999994174583 0.999917233823 0.652720315259 0.627945981812 0.449838583213 0.350441220963 0.290188220622 0.510478247707 0.510450359519 0.000082766177 0.449806446076 0.981468761133 0.852555702466 0.912481600659 0.912481600659
256 SNOWBALL_SWEDISH_DIRECT SV_SE ALL_WORDS PRIMARY_OUTPUT 12371 98108 68 98108 0 1 98108 25915 237017 67105 148325 4812088331 67105 4812155436 0.001394 148325 385342 38.491781 0.779348419384 0.615082186733 0.999986055105 0.999955235704 0.807534120919 0.739831942216 0.687539886056 0.642151948805 0.523855832838 0.692360693585 0.692339154006 0.000044764296 0.687517812951 0.984860422704 0.942685282143 0.963311451566 0.963311451566
257 SNOWBALL_SWEDISH_DIRECT SV_SE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 12342 97881 68 97881 0 1 97881 25840 236588 67105 147975 4789844472 67105 4789911577 0.001401 147975 384563 38.478741 0.779036724587 0.615212591955 0.999985990347 0.999955100897 0.807599291151 0.739644914918 0.687500000000 0.642223301999 0.523809523810 0.692295603454 0.692273994517 0.000044899103 0.687477858823 0.984821307273 0.942694565973 0.963297587020 0.963297587020
258 SNOWBALL_SWEDISH_LUCENE_FILTER SV_SE ALL_WORDS PRIMARY_OUTPUT 12371 98108 68 98108 0 1 98108 26781 230676 64262 154666 4812091174 64262 4812155436 0.001335 154666 385342 40.137333 0.782116919488 0.598626674487 0.999986645901 0.999954508853 0.799306660194 0.736939762085 0.678179573117 0.628097931391 0.513064830384 0.684248529829 0.684226838572 0.000045491147 0.678157227687 0.985207247898 0.939659207875 0.961894327137 0.961894327137
259 SNOWBALL_SWEDISH_LUCENE_FILTER SV_SE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 12342 97881 68 97881 0 1 97881 26706 230247 64262 154316 4789847315 64262 4789911577 0.001342 154316 384563 40.127625 0.781799537535 0.598723746174 0.999986583886 0.999954370671 0.799355165030 0.736743719918 0.678122496584 0.628142458291 0.512999498691 0.684165146635 0.684143384784 0.000045629329 0.678100081584 0.985169028543 0.939660518299 0.961876797342 0.961876797342
260 SNOWBALL_YIDDISH_DIRECT YI ALL_WORDS PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 1087 4962 1151 1382 6391758 1151 6392909 0.018004 1382 6344 21.784363 0.811712743334 0.782156368222 0.999819956768 0.999604172550 0.890988162495 0.805624107027 0.796660512162 0.787894185271 0.662041360907 0.796797522188 0.796599716782 0.000395827450 0.796462473806 0.982918530193 0.962014249878 0.972354049666 0.972354049666
261 SNOWBALL_YIDDISH_DIRECT YI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 1087 4962 1151 1382 6391758 1151 6392909 0.018004 1382 6344 21.784363 0.811712743334 0.782156368222 0.999819956768 0.999604172550 0.890988162495 0.805624107027 0.796660512162 0.787894185271 0.662041360907 0.796797522188 0.796599716782 0.000395827450 0.796462473806 0.982918530193 0.962014249878 0.972354049666 0.972354049666
262 SNOWBALL_YIDDISH_LUCENE_FILTER YI ALL_WORDS PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 1087 4962 1151 1382 6391758 1151 6392909 0.018004 1382 6344 21.784363 0.811712743334 0.782156368222 0.999819956768 0.999604172550 0.890988162495 0.805624107027 0.796660512162 0.787894185271 0.662041360907 0.796797522188 0.796599716782 0.000395827450 0.796462473806 0.982918530193 0.962014249878 0.972354049666 0.972354049666
263 SNOWBALL_YIDDISH_LUCENE_FILTER YI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 1087 4962 1151 1382 6391758 1151 6392909 0.018004 1382 6344 21.784363 0.811712743334 0.782156368222 0.999819956768 0.999604172550 0.890988162495 0.805624107027 0.796660512162 0.787894185271 0.662041360907 0.796797522188 0.796599716782 0.000395827450 0.796462473806 0.982918530193 0.962014249878 0.972354049666 0.972354049666
264 SPANISH_LUCENE_SPANISH_LIGHT_STEM_FILTER ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 405552 1244317 147956 40729019 379567170154 147956 379567318110 0.000039 40729019 41973336 97.035458 0.893730611741 0.029645415842 0.999999610198 0.999892318297 0.514822513020 0.130863846499 0.057387272020 0.036752000024 0.029541282827 0.162772895888 0.162762055080 0.000107681703 0.057380579619 0.993823553768 0.756690454887 0.859195405761 0.859195405761
265 SPANISH_LUCENE_SPANISH_LIGHT_STEM_FILTER ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 404617 1241848 146613 40621522 377860523152 146613 377860669765 0.000039 40621522 41863370 97.033569 0.894406108634 0.029664310351 0.999999611992 0.999892119974 0.514831961171 0.130949068412 0.057424066047 0.036775459718 0.029560783207 0.162886280533 0.162875426941 0.000107880026 0.057417362063 0.993866319748 0.756725425267 0.859233931168 0.859233931168
266 SPANISH_LUCENE_SPANISH_MINIMAL_STEM_FILTER ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 718633 148463 47859 41824873 379567270251 47859 379567318110 0.000013 41824873 41973336 99.646292 0.756221921130 0.003537078873 0.999999873912 0.999889695187 0.501768476392 0.017360591398 0.007041223811 0.004416184633 0.003533050405 0.051718628951 0.051713939443 0.000110304813 0.007040201537 0.995635307295 0.710609719902 0.829315972283 0.829315972283
267 SPANISH_LUCENE_SPANISH_MINIMAL_STEM_FILTER ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 717093 148226 47148 41715144 377860622617 47148 377860669765 0.000012 41715144 41863370 99.645929 0.758678227400 0.003540708739 0.999999875224 0.999889489251 0.501770291981 0.017379114288 0.007048522419 0.004420728101 0.003536725554 0.051829129163 0.051824445330 0.000110510749 0.007047500484 0.995675746140 0.710626433954 0.829341382913 0.829341382913
268 SPANISH_LUCENE_SPANISH_PLURAL_STEM_FILTER ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 578805 325245 58578 41648091 379567259532 58578 379567318110 0.000015 41648091 41973336 99.225115 0.847382777999 0.007748847983 0.999999845672 0.999890132644 0.503874346827 0.037377069210 0.015357262275 0.009663967067 0.007738048760 0.081032341260 0.081026288458 0.000109867356 0.015355289170 0.995442321769 0.723731297627 0.838115191065 0.838115191065
269 SPANISH_LUCENE_SPANISH_PLURAL_STEM_FILTER ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 577533 324656 57716 41538714 377860612049 57716 377860669765 0.000015 41538714 41863370 99.224487 0.849057985417 0.007755132948 0.999999847256 0.999889928152 0.503877490102 0.037408921072 0.015369880354 0.009671831022 0.007744455857 0.081145286724 0.081139234944 0.000110071848 0.015367905834 0.995484117647 0.723752971494 0.838144538380 0.838144538380
270 SPANISH_RADIXOR ES_ES ALL_WORDS PRIMARY_OUTPUT 65059 871332 3589 871332 0 1 871332 64995 41074684 288483 898652 379567029627 288483 379567318110 0.000076 898652 41973336 2.141007 0.993025606574 0.978589931475 0.999999239969 0.999996872745 0.989294585722 0.990104500109 0.985754921826 0.981443392220 0.971909988067 0.985781345071 0.985779787115 0.000003127255 0.985753358111 0.995417814373 0.993266303762 0.994340895233 0.994340895233
271 SPANISH_RADIXOR ES_ES ALL_WORDS ANY_CANDIDATE 65059 871332 3589 828695 42637 21 916797 65118 41972710 2 626 379567318108 2 379567318110 0.000000 626 41973336 0.001491 0.999999952350 0.999985085770 0.999999999995 0.999999998346 0.999992542882 0.999996978999 0.999992519005 0.999988059050 0.999985038121 0.999992519032 0.999992518205 0.000000001654
272 SPANISH_RADIXOR ES_ES ALL_WORDS ALL_CANDIDATES 65059 871332 3589 828695 42637 21 916797 65118 41972710 1349800 626 379565968310 1349800 379567318110 0.000356 626 41973336 0.001491 0.968842987168 0.999985085770 0.999996443846 0.999996442590 0.999990764808 0.974915259157 0.984167740127 0.993597526064 0.968828987818 0.984290880594 0.984289129583 0.000003557410
273 SPANISH_RADIXOR ES_ES LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 64918 869371 3525 869371 0 1 869371 64814 40978337 276044 885033 377860393721 276044 377860669765 0.000073 885033 41863370 2.114099 0.993308734895 0.978859012067 0.999999269456 0.999996927576 0.989429140761 0.990384762162 0.986030938205 0.981715226337 0.972446769193 0.986057405488 0.986055874970 0.000003072424 0.986029401906 0.995463637710 0.993323040564 0.994392187139 0.994392187139
274 SPANISH_RADIXOR ES_ES LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 64918 869371 3525 826968 42403 21 914127 64933 41863370 0 0 377860669765 0 377860669765 0.000000 0 41863370 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
275 SPANISH_RADIXOR ES_ES LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 64918 869371 3525 826968 42403 21 914127 64933 41863370 1255381 0 377859414384 1255381 377860669765 0.000332 0 41863370 0.000000 0.970885497124 1.000000000000 0.999996677662 0.999996678030 0.999998338831 0.976571978660 0.985227704543 0.994038240493 0.970885497124 0.985335220686 0.985333583876 0.000003321970
276 SWEDISH_LUCENE_SWEDISH_LIGHT_STEM_FILTER SV_SE ALL_WORDS PRIMARY_OUTPUT 12371 98108 68 98108 0 1 98108 22392 218635 45941 166707 4812109495 45941 4812155436 0.000955 166707 385342 43.262089 0.826359911708 0.567379107390 0.999990453135 0.999955813777 0.783684780262 0.757232036109 0.672807954234 0.605320541501 0.506940918144 0.684733049508 0.684712936280 0.000044186223 0.672786622564 0.986795482859 0.942302523776 0.964035907715 0.964035907715
277 SWEDISH_LUCENE_SWEDISH_LIGHT_STEM_FILTER SV_SE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 12342 97881 68 97881 0 1 97881 22338 218126 45941 166437 4789865636 45941 4789911577 0.000959 166437 384563 43.279515 0.826025213298 0.567204853301 0.999990408800 0.999955664954 0.783597631051 0.756945124029 0.672574503184 0.605125951621 0.506675896159 0.684489232882 0.684469049184 0.000044335046 0.672553099274 0.986761366955 0.942264620928 0.963999791833 0.963999791833
278 SWEDISH_LUCENE_SWEDISH_MINIMAL_STEM_FILTER SV_SE ALL_WORDS PRIMARY_OUTPUT 12371 98108 68 98108 0 1 98108 23360 228181 40227 157161 4812115209 40227 4812155436 0.000836 157161 385342 40.784809 0.850127417961 0.592151906618 0.999991640544 0.999958984659 0.796071773581 0.781991317186 0.698068068834 0.630412272016 0.536178622033 0.709510092538 0.709491456160 0.000041015341 0.698048215965 0.988492665376 0.944581755622 0.966038479572 0.966038479572
279 SWEDISH_LUCENE_SWEDISH_MINIMAL_STEM_FILTER SV_SE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 12342 97881 68 97881 0 1 97881 23312 227624 40227 156939 4789871350 40227 4789911577 0.000840 156939 384563 40.809698 0.849815755775 0.591903017191 0.999991601724 0.999958840540 0.795947309457 0.781693541131 0.697790053555 0.630152322431 0.535850655618 0.709230928471 0.709212225121 0.000041159460 0.697770131116 0.988462934404 0.944527865197 0.965996098328 0.965996098328
280 SWEDISH_RADIXOR SV_SE ALL_WORDS PRIMARY_OUTPUT 12371 98108 68 98108 0 1 98108 12330 365796 24473 19546 4812130963 24473 4812155436 0.000509 19546 385342 5.072377 0.937291970410 0.949276227351 0.999994914337 0.999990853272 0.974635570844 0.939664553041 0.943246034417 0.946854921499 0.892588119029 0.943265066457 0.943260495884 0.000009146728 0.943241460869 0.992630770222 0.993394969179 0.993012722673 0.993012722673
281 SWEDISH_RADIXOR SV_SE ALL_WORDS ANY_CANDIDATE 12371 98108 68 92341 5767 5 104148 12371 385342 0 0 4812155436 0 4812155436 0.000000 0 385342 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
282 SWEDISH_RADIXOR SV_SE ALL_WORDS ALL_CANDIDATES 12371 98108 68 92341 5767 5 104148 12371 385342 47848 0 4812107588 47848 4812155436 0.000994 0 385342 0.000000 0.889545003347 1.000000000000 0.999990056847 0.999990057643 0.999995028423 0.909639856815 0.941544130223 0.975767741439 0.889545003347 0.943156934634 0.943152245645 0.000009942357
283 SWEDISH_RADIXOR SV_SE LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 12342 97881 68 97881 0 1 97881 12301 365017 24473 19546 4789887104 24473 4789911577 0.000511 19546 384563 5.082652 0.937166551131 0.949173477428 0.999994890720 0.999990810798 0.974584184074 0.939543572972 0.943131801052 0.946747541943 0.892383555482 0.943150907472 0.943146315681 0.000009189202 0.943127206266 0.992611730682 0.993377890892 0.992994663001 0.992994663001
284 SWEDISH_RADIXOR SV_SE LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 12342 97881 68 92114 5767 5 103921 12342 384563 0 0 4789911577 0 4789911577 0.000000 0 384563 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
285 SWEDISH_RADIXOR SV_SE LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 12342 97881 68 92114 5767 5 103921 12342 384563 47848 0 4789863729 47848 4789911577 0.000999 0 384563 0.000000 0.889346015712 1.000000000000 0.999990010672 0.999990011473 0.999995005336 0.909473386475 0.941432652692 0.975719846569 0.889346015712 0.943051438529 0.943046728292 0.000009988527
286 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA ALL_WORDS PRIMARY_OUTPUT 1493 14245 4 14245 0 1 14245 2358 56032 828 9308 101386722 828 101387550 0.000817 9308 65340 14.245485 0.985437917693 0.857545148454 0.999991833317 0.999900091560 0.928768490886 0.956895962839 0.917054009820 0.880397209478 0.846814169992 0.919270093835 0.919222898475 0.000099908440 0.917004266345 0.997989675681 0.970999849348 0.984309781661 0.984309781661
287 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA ALL_WORDS ANY_CANDIDATE 1493 14245 4 12038 2207 6 16937 2912 60394 122 4946 101387428 122 101387550 0.000120 4946 65340 7.569636 0.997984004230 0.924303642485 0.999998796696 0.999950045780 0.962151219591 0.982322936592 0.959731756929 0.938156308641 0.922581039382 0.960437530635 0.960413432420 0.000049954220
288 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA ALL_WORDS ALL_CANDIDATES 1493 14245 4 12038 2207 6 16937 2912 60394 1368 4946 101386182 1368 101387550 0.001349 4946 65340 7.569636 0.977850458211 0.924303642485 0.999986507219 0.999937764217 0.962145074852 0.966650447520 0.950323362339 0.934538657225 0.905348683816 0.950700131656 0.950669478973 0.000062235783
289 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 1491 14236 4 14236 0 1 14236 2356 56016 828 9308 101258578 828 101259406 0.000818 9308 65324 14.248974 0.985433818873 0.857510256567 0.999991822982 0.999899965191 0.928751039775 0.956884181756 0.917032283413 0.880367133966 0.846777119361 0.919249480202 0.919202225823 0.000100034809 0.916982477117 0.997988093697 0.970977645923 0.984297603943 0.984297603943
290 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 1491 14236 4 12029 2207 6 16928 2910 60378 122 4946 101259284 122 101259406 0.000120 4946 65324 7.571490 0.997983471074 0.924285101953 0.999998795174 0.999949982596 0.962141948563 0.982318335047 0.959721515768 0.938140934008 0.922562112276 0.960427641371 0.960403512884 0.000050017404
291 UKRAINIAN_LUCENE_MORFOLOGIK_FILTER UK_UA LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 1491 14236 4 12029 2207 6 16928 2910 60378 1368 4946 101258038 1368 101259406 0.001351 4946 65324 7.571490 0.977844718686 0.924285101953 0.999986490144 0.999937685499 0.962135796049 0.966641904786 0.950310852286 0.934522445998 0.905325976129 0.950687806541 0.950657115187 0.000062314501
292 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA ALL_WORDS PRIMARY_OUTPUT 1493 14245 4 14245 0 1 14245 2365 56016 828 9324 101386722 828 101387550 0.000817 9324 65340 14.269972 0.985433818873 0.857300275482 0.999991833317 0.999899933851 0.928646054399 0.956831877998 0.916912197996 0.880190066750 0.846572361262 0.919136923635 0.919089660097 0.000100066149 0.916862376978 0.997989675681 0.970875945953 0.984246115917 0.984246115917
293 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA ALL_WORDS ANY_CANDIDATE 1493 14245 4 12038 2207 6 16937 2919 60378 122 4962 101387428 122 101387550 0.000120 4962 65340 7.594123 0.997983471074 0.924058769513 0.999998796696 0.999949888071 0.962028783105 0.982267195939 0.959599491418 0.937954390108 0.922336622774 0.960310042786 0.960285871670 0.000050111929
294 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA ALL_WORDS ALL_CANDIDATES 1493 14245 4 12038 2207 6 16937 2919 60378 1368 4962 101386182 1368 101387550 0.001349 4962 65340 7.594123 0.977844718686 0.924058769513 0.999986507219 0.999937606509 0.962022638366 0.966592384831 0.950191209102 0.934337338211 0.905108832524 0.950571400540 0.950540673331 0.000062393491
295 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 1491 14236 4 14236 0 1 14236 2356 56016 828 9308 101258578 828 101259406 0.000818 9308 65324 14.248974 0.985433818873 0.857510256567 0.999991822982 0.999899965191 0.928751039775 0.956884181756 0.917032283413 0.880367133966 0.846777119361 0.919249480202 0.919202225823 0.000100034809 0.916982477117 0.997988093697 0.970977645923 0.984297603943 0.984297603943
296 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 1491 14236 4 12029 2207 6 16928 2910 60378 122 4946 101259284 122 101259406 0.000120 4946 65324 7.571490 0.997983471074 0.924285101953 0.999998795174 0.999949982596 0.962141948563 0.982318335047 0.959721515768 0.938140934008 0.922562112276 0.960427641371 0.960403512884 0.000050017404
297 UKRAINIAN_MORFOLOGIK_DIRECT UK_UA LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 1491 14236 4 12029 2207 6 16928 2910 60378 1368 4946 101258038 1368 101259406 0.001351 4946 65324 7.571490 0.977844718686 0.924285101953 0.999986490144 0.999937685499 0.962135796049 0.966641904786 0.950310852286 0.934522445998 0.905325976129 0.950687806541 0.950657115187 0.000062314501
298 UKRAINIAN_RADIXOR UK_UA ALL_WORDS PRIMARY_OUTPUT 1493 14245 4 14245 0 1 14245 1493 64732 880 608 101386670 880 101387550 0.000868 608 65340 0.930517 0.986587819301 0.990694827058 0.999991320433 0.999985333094 0.995343073746 0.987406494442 0.988637057853 0.989870692292 0.977529447297 0.988639190514 0.988631855097 0.000014666906 0.988629719696 0.997993591453 0.998265712624 0.998129633491 0.998129633491
299 UKRAINIAN_RADIXOR UK_UA ALL_WORDS ANY_CANDIDATE 1493 14245 4 14055 190 2 14435 1493 65340 0 0 101387550 0 101387550 0.000000 0 65340 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
300 UKRAINIAN_RADIXOR UK_UA ALL_WORDS ALL_CANDIDATES 1493 14245 4 14055 190 2 14435 1493 65340 1490 0 101386060 1490 101387550 0.001470 0 65340 0.000000 0.977704623672 1.000000000000 0.999985303916 0.999985313380 0.999992651958 0.982083809295 0.988726639933 0.995459946982 0.977704623672 0.988789473888 0.988782208195 0.000014686620
301 UKRAINIAN_RADIXOR UK_UA LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 1491 14236 4 14236 0 1 14236 1491 64716 880 608 101258526 880 101259406 0.000869 608 65324 0.930745 0.986584547838 0.990692547915 0.999991309449 0.999985314543 0.995341928682 0.987403420118 0.988634280477 0.989868213355 0.977524016676 0.988636414174 0.988629069474 0.000014685457 0.988626933033 0.997992012551 0.998264347489 0.998128161444 0.998128161444
302 UKRAINIAN_RADIXOR UK_UA LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 1491 14236 4 14046 190 2 14426 1491 65324 0 0 101259406 0 101259406 0.000000 0 65324 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
303 UKRAINIAN_RADIXOR UK_UA LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 1491 14236 4 14046 190 2 14426 1491 65324 1490 0 101257916 1490 101259406 0.001471 0 65324 0.000000 0.977699284581 1.000000000000 0.999985285318 0.999985294804 0.999992642659 0.982079499669 0.988723909852 0.995458840023 0.977699284581 0.988786774073 0.988779499204 0.000014705196
304 YI_RADIXOR YI ALL_WORDS PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 802 6195 195 149 6392714 195 6392909 0.003050 149 6344 2.348676 0.969483568075 0.976513240858 0.999969497454 0.999946243726 0.988241369156 0.970881394183 0.972985707555 0.975099162627 0.947392567671 0.972992055990 0.972965163911 0.000053756274 0.972958803000 0.995691103897 0.996142223728 0.995916612726 0.995916612726
305 YI_RADIXOR YI ALL_WORDS ANY_CANDIDATE 802 3578 0 3489 89 3 3676 802 6344 0 0 6392909 0 6392909 0.000000 0 6344 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
306 YI_RADIXOR YI ALL_WORDS ALL_CANDIDATES 802 3578 0 3489 89 3 3676 802 6344 389 0 6392520 389 6392909 0.006085 0 6344 0.000000 0.942224862617 1.000000000000 0.999939151332 0.999939211655 0.999969575666 0.953239572064 0.970253116158 0.987885016662 0.942224862617 0.970682678643 0.970653145819 0.000060788345
307 YI_RADIXOR YI LOWERCASE_GROUPS_ONLY PRIMARY_OUTPUT 802 3578 0 3578 0 1 3578 802 6195 195 149 6392714 195 6392909 0.003050 149 6344 2.348676 0.969483568075 0.976513240858 0.999969497454 0.999946243726 0.988241369156 0.970881394183 0.972985707555 0.975099162627 0.947392567671 0.972992055990 0.972965163911 0.000053756274 0.972958803000 0.995691103897 0.996142223728 0.995916612726 0.995916612726
308 YI_RADIXOR YI LOWERCASE_GROUPS_ONLY ANY_CANDIDATE 802 3578 0 3489 89 3 3676 802 6344 0 0 6392909 0 6392909 0.000000 0 6344 0.000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 1.000000000000 0.000000000000
309 YI_RADIXOR YI LOWERCASE_GROUPS_ONLY ALL_CANDIDATES 802 3578 0 3489 89 3 3676 802 6344 389 0 6392520 389 6392909 0.006085 0 6344 0.000000 0.942224862617 1.000000000000 0.999939151332 0.999939211655 0.999969575666 0.953239572064 0.970253116158 0.987885016662 0.942224862617 0.970682678643 0.970653145819 0.000060788345

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# Benchmark Results
This section contains the published Radixor benchmark reference set. It is intentionally split into
two layers:
- **benchmark reference pages**, which explain methodology, corpora, environment, candidate
selection, and the English dictionary coverage experiment;
- **language result pages**, which contain the actual same-language accuracy and throughput tables.
- **pairwise quality pages and generated sections**, which publish over-stemming, under-stemming,
candidate-policy, classification, and partition measurements from one checked result snapshot.
This structure keeps methodology separate from per-language result pages, while preserving all
measured data and the command-class analysis for each Radixor language resource.
## Read This First
Start with [Benchmarking](../benchmarking.md) for the high-level interpretation model. The most
important rule is that speed and exact-root quality must be read together. Many competing stemmers
are intentionally light, minimal, or aggressive; they can be fast because they are not trying to
match dictionary roots with the same precision.
Radixor rows in the refreshed tables use contracted compiled patch tries. Contraction collapses
uniform preferred-command subtrees into accepting leaves, reducing hot lookup depth while preserving
the preferred result measured by the accuracy pass.
## Reference Pages
| Page | Purpose |
| --- | --- |
| [Methodology](reference/methodology.md) | Workload design, normalization, speed metrics, quality metrics, and interpretation rules. |
| [Linguistic quality methodology](reference/linguistic-quality.md) | Gold-standard groups, output policies, pairwise formulas, ranking rules, aggregation, and limitations. |
| [Tested stemmers](reference/tested-stemmers.md) | Versions, upstream attribution, evaluated coverage, adapters, preprocessing, and output capability. |
| [Reproducibility and raw data](reference/reproducibility.md) | Commands, versioned CSV snapshot, checksum, generated artifacts, and unavailable provenance. |
| [Corpora](reference/corpora.md) | Dictionary row counts, complete quality tokens, already-root tokens, changed speed tokens, and timing token counts. |
| [Environment and reports](reference/environment.md) | Hardware, JVM, JMH settings, report files, and badge/report policy. |
| [English dictionary coverage](reference/english-coverage.md) | Quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |
| [Candidate evaluation](reference/candidates.md) | Included benchmark families and evaluated candidates that were skipped. |
## Language Results
Each language page contains:
- the dictionary corpus size,
- the Radixor patch-command distribution,
- exact-root quality metrics,
- throughput metrics,
- interpretation notes for the compared stemmers.
Open [Language Benchmark Pages](languages/index.md) for the complete language list.
## Key Published Result
The English dictionary coverage benchmark shows the current contracted-trie operating curve. With
the full English dictionary, Radixor reaches `97.478%` all-token exactness and `97.197%`
changed-token exactness at `135.8 ns/token`. Even with a deterministic 10% dictionary slice, it
keeps `92.868%` all-token exactness and `76.516%` changed-token exactness at `86.0 ns/token`.
Those figures should not be reduced to a single speed badge. The professional interpretation is a
quality/speed envelope: the amount and quality of dictionary knowledge affect stemming precision,
while contracted tries reduce lookup cost in uniform regions of the compiled graph.
## Quality versus performance
Each language page keeps exact-root accuracy, JMH latency, and pairwise linguistic-quality results in separate tables. No undocumented scalar combines them. The current repository checkout does not contain the dated machine-readable JMH CSV files named by the performance provenance page, so this revision preserves the existing performance tables but does not regenerate a cross-language Pareto frontier from rounded Markdown values. A defensible Pareto analysis requires the original unrounded JMH snapshot on the same hardware and JVM. Readers can still inspect the quality and speed dimensions side by side on every language page.
<!-- STEMMING-QUALITY-OVERVIEW:START -->
## Pairwise Quality Findings
The validated snapshot is a broad multilingual comparison covering the complete 20-language Radixor dictionary universe; 19 languages have existing benchmark pages. The direct ranking below uses only deterministic `PRIMARY_OUTPUT` rows over identical per-language inputs. Candidate-aware rows are intentionally excluded from this claim.
!!! success "Evidence-based primary-output result"
Radixor achieved the highest balanced accuracy among the evaluated deterministic stemmers for every documented language in both `ALL_WORDS` and `LOWERCASE_GROUPS_ONLY`: **38 wins in 38 language-mode comparisons, with no exact first-place ties**. This statement is limited to the evaluated implementations, versions, dictionaries, adapters, and balanced-accuracy metric; it is not a universal claim about every stemming use case.
### Per-language winner matrix
| Language | Dictionary mode | Winner | Balanced accuracy | Runner-up | Difference | Exact tie | Deterministic stemmers |
|---|---|---|---:|---|---:|---|---:|
|Czech (`CS_CZ`)|ALL_WORDS|Radixor|0.996565|HUNSPELL CZECH LUCENE FILTER|0.142812638|no|3|
|Czech (`CS_CZ`)|LOWERCASE_GROUPS_ONLY|Radixor|0.997139|HUNSPELL CZECH LUCENE FILTER|0.144369049|no|3|
|Danish (`DA_DK`)|ALL_WORDS|Radixor|0.996066|SNOWBALL DANISH LUCENE FILTER|0.058096771|no|3|
|Danish (`DA_DK`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996305|SNOWBALL DANISH DIRECT|0.058230346|no|3|
|Dutch (`NL_NL`)|ALL_WORDS|Radixor|0.988661|SNOWBALL DUTCH DIRECT|0.261574077|no|4|
|Dutch (`NL_NL`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989040|SNOWBALL DUTCH DIRECT|0.258544404|no|4|
|English (`US_UK`)|ALL_WORDS|Radixor|0.965159|ENGLISH LUCENE PORTER COPIED|0.010532535|no|11|
|English (`US_UK`)|LOWERCASE_GROUPS_ONLY|Radixor|0.965820|ENGLISH LUCENE PORTER COPIED|0.010920064|no|11|
|Finnish (`FI_FI`)|ALL_WORDS|Radixor|0.984594|SNOWBALL FINNISH LUCENE FILTER|0.244241861|no|4|
|Finnish (`FI_FI`)|LOWERCASE_GROUPS_ONLY|Radixor|0.988068|SNOWBALL FINNISH DIRECT|0.249668284|no|4|
|French (`FR_FR`)|ALL_WORDS|Radixor|0.956992|SNOWBALL FRENCH DIRECT|0.111730673|no|6|
|French (`FR_FR`)|LOWERCASE_GROUPS_ONLY|Radixor|0.957224|SNOWBALL FRENCH DIRECT|0.111809799|no|6|
|German (`DE_DE`)|ALL_WORDS|Radixor|0.907901|GERMAN CISTEM|0.027131083|no|8|
|German (`DE_DE`)|LOWERCASE_GROUPS_ONLY|Radixor|0.966157|GERMAN CISTEM|0.050868631|no|8|
|Hungarian (`HU_HU`)|ALL_WORDS|Radixor|0.995491|SNOWBALL HUNGARIAN LUCENE FILTER|0.172884951|no|4|
|Hungarian (`HU_HU`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996163|SNOWBALL HUNGARIAN DIRECT|0.174455479|no|4|
|Italian (`IT_IT`)|ALL_WORDS|Radixor|0.996507|SNOWBALL ITALIAN DIRECT|0.130318040|no|4|
|Italian (`IT_IT`)|LOWERCASE_GROUPS_ONLY|Radixor|0.996512|SNOWBALL ITALIAN DIRECT|0.130307087|no|4|
|Norwegian Bokmal (`NB_NO`)|ALL_WORDS|Radixor|0.974783|SNOWBALL NORWEGIAN BOKMAL DIRECT|0.099819340|no|5|
|Norwegian Bokmal (`NB_NO`)|LOWERCASE_GROUPS_ONLY|Radixor|0.975000|SNOWBALL NORWEGIAN BOKMAL DIRECT|0.100008544|no|5|
|Norwegian Nynorsk (`NN_NO`)|ALL_WORDS|Radixor|0.935777|SNOWBALL NORWEGIAN NYNORSK DIRECT|0.076868986|no|3|
|Norwegian Nynorsk (`NN_NO`)|LOWERCASE_GROUPS_ONLY|Radixor|0.935853|SNOWBALL NORWEGIAN NYNORSK DIRECT|0.076816096|no|3|
|Persian (`FA_IR`)|ALL_WORDS|Radixor|0.974922|PERSIAN LUCENE PERSIAN STEM FILTER|0.472751327|no|2|
|Persian (`FA_IR`)|LOWERCASE_GROUPS_ONLY|Radixor|0.974922|PERSIAN LUCENE PERSIAN STEM FILTER|0.472751327|no|2|
|Polish (`PL_PL`)|ALL_WORDS|Radixor|0.990388|POLISH LUCENE MORFOLOGIK FILTER|0.042233990|no|5|
|Polish (`PL_PL`)|LOWERCASE_GROUPS_ONLY|Radixor|0.990579|POLISH LUCENE MORFOLOGIK FILTER|0.042401633|no|5|
|Portuguese (`PT_PT`)|ALL_WORDS|Radixor|0.998502|SNOWBALL PORTUGUESE DIRECT|0.059701750|no|6|
|Portuguese (`PT_PT`)|LOWERCASE_GROUPS_ONLY|Radixor|0.998502|SNOWBALL PORTUGUESE DIRECT|0.059701750|no|6|
|Russian (`RU_RU`)|ALL_WORDS|Radixor|0.989827|SNOWBALL RUSSIAN LUCENE FILTER|0.154951419|no|4|
|Russian (`RU_RU`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989852|SNOWBALL RUSSIAN DIRECT|0.154997931|no|4|
|Spanish (`ES_ES`)|ALL_WORDS|Radixor|0.989295|SNOWBALL SPANISH LUCENE FILTER|0.336680479|no|7|
|Spanish (`ES_ES`)|LOWERCASE_GROUPS_ONLY|Radixor|0.989429|SNOWBALL SPANISH DIRECT|0.336708826|no|7|
|Swedish (`SV_SE`)|ALL_WORDS|Radixor|0.974636|SNOWBALL SWEDISH DIRECT|0.167101450|no|5|
|Swedish (`SV_SE`)|LOWERCASE_GROUPS_ONLY|Radixor|0.974584|SNOWBALL SWEDISH DIRECT|0.166984893|no|5|
|Ukrainian (`UK_UA`)|ALL_WORDS|Radixor|0.995343|UKRAINIAN LUCENE MORFOLOGIK FILTER|0.066574583|no|4|
|Ukrainian (`UK_UA`)|LOWERCASE_GROUPS_ONLY|Radixor|0.995342|UKRAINIAN LUCENE MORFOLOGIK FILTER|0.066590889|no|4|
|Yiddish (`YI`)|ALL_WORDS|Radixor|0.988241|SNOWBALL YIDDISH DIRECT|0.097253207|no|3|
|Yiddish (`YI`)|LOWERCASE_GROUPS_ONLY|Radixor|0.988241|SNOWBALL YIDDISH DIRECT|0.097253207|no|3|
### Secondary-metric trade-offs
Balanced-accuracy leadership does not imply leadership on every error trade-off. The table below lists all **15** deterministic primary-output language-mode-metric cases where a non-Radixor adapter has the best displayed value. Equal values are resolved by the authoritative row ordering and should be read as ties when the unrounded values are equal. Throughput leadership remains in the separate performance tables.
<details class="quality-details" markdown="1"><summary>Non-Radixor secondary-metric leaders</summary>
| Language | Dictionary mode | Metric | Leader | Value |
|---|---|---|---|---:|
|English|ALL_WORDS|Over-stemming percentage|ENGLISH LUCENE POSSESSIVE FILTER|0.000604|
|English|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|ENGLISH LUCENE POSSESSIVE FILTER|0.000653|
|French|ALL_WORDS|Over-stemming percentage|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|0.000177|
|French|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|0.000166|
|German|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|0.000188|
|Italian|ALL_WORDS|Over-stemming percentage|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|0.000020|
|Italian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|0.000020|
|Persian|ALL_WORDS|Over-stemming percentage|PERSIAN LUCENE PERSIAN STEM FILTER|0.002652|
|Persian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|PERSIAN LUCENE PERSIAN STEM FILTER|0.002652|
|Portuguese|ALL_WORDS|Over-stemming percentage|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|0.000003|
|Portuguese|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|0.000003|
|Spanish|ALL_WORDS|Over-stemming percentage|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|0.000013|
|Spanish|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|0.000012|
|Ukrainian|ALL_WORDS|Over-stemming percentage|HUNSPELL UKRAINIAN LUCENE FILTER|0.000783|
|Ukrainian|LOWERCASE_GROUPS_ONLY|Over-stemming percentage|HUNSPELL UKRAINIAN LUCENE FILTER|0.000784|
</details>
### Win, tie, and placement summary
Counts use `PRIMARY_OUTPUT` only and retain each adapter configuration as a separate stemmer except that language-specific Radixor identifiers are combined as Radixor. Coverage is displayed explicitly; unsupported languages are absent, not losses.
<details class="quality-details" markdown="1"><summary>ALL_WORDS placements</summary>
| Stemmer | Evaluated languages | Wins | Exact first-place ties | Top-three placements | Average rank | Median rank |
|---|---:|---:|---:|---:|---:|---:|
|Radixor|19|19|0|19|1.000|1.000|
|CZECH LUCENE CZECH STEM FILTER|1|0|0|1|3.000|3.000|
|ENGLISH LUCENE KSTEM FILTER|1|0|0|0|8.000|8.000|
|ENGLISH LUCENE MINIMAL FILTER|1|0|0|0|9.000|9.000|
|ENGLISH LUCENE PORTER COPIED|1|0|0|1|2.000|2.000|
|ENGLISH LUCENE PORTER FILTER|1|0|0|1|3.000|3.000|
|ENGLISH LUCENE POSSESSIVE FILTER|1|0|0|0|11.000|11.000|
|ENGLISH OPENNLP PORTER|1|0|0|0|4.000|4.000|
|ENGLISH PAICE HUSK LANCASTER|1|0|0|0|7.000|7.000|
|ENGLISH SNOWBALL ORIGINAL PORTER|1|0|0|0|6.000|6.000|
|ENGLISH SNOWBALL PORTER2|1|0|0|0|5.000|5.000|
|FINNISH LUCENE FINNISH LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|FRENCH LUCENE FRENCH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
|GERMAN CISTEM|1|0|0|1|2.000|2.000|
|GERMAN LUCENE GERMAN LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|1|0|0|0|8.000|8.000|
|GERMAN LUCENE GERMAN STEM FILTER|1|0|0|0|6.000|6.000|
|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL CZECH LUCENE FILTER|1|0|0|1|2.000|2.000|
|HUNSPELL DUTCH LUCENE FILTER|1|0|0|1|3.000|3.000|
|HUNSPELL ENGLISH LUCENE FILTER|1|0|0|0|10.000|10.000|
|HUNSPELL FRENCH LUCENE FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL GERMAN LUCENE FILTER|1|0|0|0|7.000|7.000|
|HUNSPELL POLISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|HUNSPELL SPANISH LUCENE FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL UKRAINIAN LUCENE FILTER|1|0|0|0|4.000|4.000|
|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|1|0|0|0|5.000|5.000|
|PERSIAN LUCENE PERSIAN STEM FILTER|1|0|0|1|2.000|2.000|
|POLISH LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
|POLISH LUCENE STEMPEL DIRECT|1|0|0|0|4.000|4.000|
|POLISH LUCENE STEMPEL FILTER|1|0|0|0|5.000|5.000|
|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|1|0|0|0|4.000|4.000|
|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL DANISH DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL DANISH LUCENE FILTER|1|0|0|1|2.000|2.000|
|SNOWBALL DUTCH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL DUTCH LUCENE FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL FINNISH DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL FINNISH LUCENE FILTER|1|0|0|1|2.000|2.000|
|SNOWBALL FRENCH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL FRENCH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL GERMAN DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL GERMAN LUCENE FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL HUNGARIAN DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL HUNGARIAN LUCENE FILTER|1|0|0|1|2.000|2.000|
|SNOWBALL ITALIAN DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL ITALIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL NORWEGIAN BOKMAL DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL NORWEGIAN NYNORSK DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL PORTUGUESE DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL PORTUGUESE LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL RUSSIAN DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL RUSSIAN LUCENE FILTER|1|0|0|1|2.000|2.000|
|SNOWBALL SPANISH DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL SPANISH LUCENE FILTER|1|0|0|1|2.000|2.000|
|SNOWBALL SWEDISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL SWEDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL YIDDISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL YIDDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SPANISH LUCENE SPANISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|1|0|0|0|7.000|7.000|
|SPANISH LUCENE SPANISH PLURAL STEM FILTER|1|0|0|0|6.000|6.000|
|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|1|0|0|0|4.000|4.000|
|UKRAINIAN LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
|UKRAINIAN MORFOLOGIK DIRECT|1|0|0|1|3.000|3.000|
</details>
<details class="quality-details" markdown="1"><summary>LOWERCASE_GROUPS_ONLY placements</summary>
| Stemmer | Evaluated languages | Wins | Exact first-place ties | Top-three placements | Average rank | Median rank |
|---|---:|---:|---:|---:|---:|---:|
|Radixor|19|19|0|19|1.000|1.000|
|CZECH LUCENE CZECH STEM FILTER|1|0|0|1|3.000|3.000|
|ENGLISH LUCENE KSTEM FILTER|1|0|0|0|8.000|8.000|
|ENGLISH LUCENE MINIMAL FILTER|1|0|0|0|9.000|9.000|
|ENGLISH LUCENE PORTER COPIED|1|0|0|1|2.000|2.000|
|ENGLISH LUCENE PORTER FILTER|1|0|0|1|3.000|3.000|
|ENGLISH LUCENE POSSESSIVE FILTER|1|0|0|0|11.000|11.000|
|ENGLISH OPENNLP PORTER|1|0|0|0|4.000|4.000|
|ENGLISH PAICE HUSK LANCASTER|1|0|0|0|7.000|7.000|
|ENGLISH SNOWBALL ORIGINAL PORTER|1|0|0|0|6.000|6.000|
|ENGLISH SNOWBALL PORTER2|1|0|0|0|5.000|5.000|
|FINNISH LUCENE FINNISH LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|FRENCH LUCENE FRENCH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
|GERMAN CISTEM|1|0|0|1|2.000|2.000|
|GERMAN LUCENE GERMAN LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|1|0|0|0|8.000|8.000|
|GERMAN LUCENE GERMAN STEM FILTER|1|0|0|0|6.000|6.000|
|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL CZECH LUCENE FILTER|1|0|0|1|2.000|2.000|
|HUNSPELL DUTCH LUCENE FILTER|1|0|0|1|3.000|3.000|
|HUNSPELL ENGLISH LUCENE FILTER|1|0|0|0|10.000|10.000|
|HUNSPELL FRENCH LUCENE FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL GERMAN LUCENE FILTER|1|0|0|0|7.000|7.000|
|HUNSPELL POLISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|HUNSPELL SPANISH LUCENE FILTER|1|0|0|0|4.000|4.000|
|HUNSPELL UKRAINIAN LUCENE FILTER|1|0|0|0|4.000|4.000|
|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|1|0|0|0|5.000|5.000|
|PERSIAN LUCENE PERSIAN STEM FILTER|1|0|0|1|2.000|2.000|
|POLISH LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
|POLISH LUCENE STEMPEL DIRECT|1|0|0|0|4.000|4.000|
|POLISH LUCENE STEMPEL FILTER|1|0|0|0|5.000|5.000|
|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|1|0|0|0|6.000|6.000|
|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|1|0|0|0|4.000|4.000|
|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL DANISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL DANISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL DUTCH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL DUTCH LUCENE FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL FINNISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL FINNISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL FRENCH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL FRENCH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL GERMAN DIRECT|1|0|0|1|3.000|3.000|
|SNOWBALL GERMAN LUCENE FILTER|1|0|0|0|4.000|4.000|
|SNOWBALL HUNGARIAN DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL HUNGARIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL ITALIAN DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL ITALIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL NORWEGIAN BOKMAL DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL NORWEGIAN NYNORSK DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL PORTUGUESE DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL PORTUGUESE LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL RUSSIAN DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL RUSSIAN LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL SPANISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL SPANISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL SWEDISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL SWEDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SNOWBALL YIDDISH DIRECT|1|0|0|1|2.000|2.000|
|SNOWBALL YIDDISH LUCENE FILTER|1|0|0|1|3.000|3.000|
|SPANISH LUCENE SPANISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|1|0|0|0|7.000|7.000|
|SPANISH LUCENE SPANISH PLURAL STEM FILTER|1|0|0|0|6.000|6.000|
|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|1|0|0|0|5.000|5.000|
|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|1|0|0|0|4.000|4.000|
|UKRAINIAN LUCENE MORFOLOGIK FILTER|1|0|0|1|2.000|2.000|
|UKRAINIAN MORFOLOGIK DIRECT|1|0|0|1|3.000|3.000|
</details>
### Radixor full-coverage aggregates
These aggregates cover all 19 documented languages. Macro balanced accuracy gives each language equal weight. Micro metrics first sum raw pair counts across languages. Unsupported third-party languages are never inserted as zero results, so this full-coverage table is not presented as a cross-stemmer common-language ranking.
| Dictionary mode | Languages | Macro balanced accuracy | Micro balanced accuracy | Micro precision | Micro recall | Micro F1 |
|---|---:|---:|---:|---:|---:|---:|
|ALL_WORDS|19|0.978929|0.987664|0.975113|0.975328|0.975221|
|LOWERCASE_GROUPS_ONLY|19|0.982354|0.989366|0.975322|0.978734|0.977025|
### Reproducible data
- [Machine-readable quality snapshot](data/stemming-quality.csv)
- SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- [Linguistic quality methodology](reference/linguistic-quality.md)
- [Tested stemmer inventory](reference/tested-stemmers.md)
- [Reproducibility and raw data](reference/reproducibility.md)
- Pearson and Spearman correlation files are generated under `build/reports/stemming-quality/`; they are separated by dictionary mode and output policy. Correlation does not establish metric equivalence.
<!-- STEMMING-QUALITY-OVERVIEW:END -->

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# Czech Stemmer Benchmarks
This page reports same-language stemming benchmarks for Czech. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `CS_CZ` | 5,113 | 56,612 | 10,049 | 46,563 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **56,612**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 675 | 1.192% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 22,681 | 40.064% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 14,980 | 26.461% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 10,109 | 17.857% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 8,167 | 14.426% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.465% | 99.439% | 99.582% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 84.850% | 82.269% | 96.806% | Benchmark-only Czech Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene CzechStemFilter | 16.784% | 15.538% | 22.559% | Lucene Czech suffix stemmer implemented as a TokenFilter. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `czechRadixor` | 3.332 | 0.240 | 71.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 346.819 | 3.622 | 7448.4 | 104.091 | Benchmark-only Czech Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene CzechStemFilter | `czechLuceneCzechStemFilter` | 3.163 | 0.253 | 67.9 | 0.949 | Czech suffix stemmer implemented as a Lucene TokenFilter. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `CS_CZ` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/cs_cz/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996565** among 3 deterministic stemmers. The runner-up is `HUNSPELL CZECH LUCENE FILTER` at 0.853752, a difference of 0.142813. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.997139** among 3 deterministic stemmers. The runner-up is `HUNSPELL CZECH LUCENE FILTER` at 0.852770, a difference of 0.144369. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **7 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996565|3867 / 1334876815 (0.000290%)|2073 / 301835 (0.686799%)|0.988432|0.990189|0.990191|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.853752|11408 / 1334876815 (0.000855%)|88283 / 301835 (29.248762%)|0.888560|0.810759|0.819499|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.793614|14480 / 1334876815 (0.001085%)|124586 / 301835 (41.276194%)|0.829234|0.718241|0.736765|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987264|0.993132|0.999997|0.996565|0.999996|0.000004|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.949289|0.707512|0.999991|0.853752|0.999925|0.000075|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.924477|0.587238|0.999989|0.793614|0.999896|0.000104|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988432|0.990189|0.991953|0.980569|0.990194|0.990191|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.888560|0.810759|0.745486|0.681745|0.819533|0.819499|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.829234|0.718241|0.633453|0.560356|0.736809|0.736765|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990187|0.998733|0.998686|0.998709|0.998709|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.810723|0.995777|0.952852|0.973842|0.973842|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.718192|0.993801|0.944977|0.968774|0.968774|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|299762|3867|2073|1334872948|3867 / 1334876815|2073 / 301835|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|213552|11408|88283|1334865407|11408 / 1334876815|88283 / 301835|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|177249|14480|124586|1334862335|14480 / 1334876815|124586 / 301835|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1334876815 (0.000000%)|0 / 301835 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.871577|10102 / 1334876815 (0.000757%)|77523 / 301835 (25.683900%)|0.904855|0.836596|0.843258|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.956905|0.743161|0.999992|0.871577|0.999934|0.000066|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.904855|0.836596|0.777914|0.719094|0.843288|0.843258|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|301835|0|0|1334876815|0 / 1334876815|0 / 301835|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|224312|10102|77523|1334866713|10102 / 1334876815|77523 / 301835|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|5850 / 1334876815 (0.000438%)|0 / 301835 (0.000000%)|0.984732|0.990402|0.990446|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.871575|13917 / 1334876815 (0.001043%)|77523 / 301835 (25.683900%)|0.893851|0.830687|0.836477|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.980987|1.000000|0.999996|0.999998|0.999996|0.000004|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.941581|0.743161|0.999990|0.871575|0.999932|0.000068|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.984732|0.990402|0.996139|0.980987|0.990448|0.990446|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.893851|0.830687|0.775861|0.710406|0.836509|0.836477|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|301835|5850|0|1334870965|5850 / 1334876815|0 / 301835|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|224312|13917|77523|1334862898|13917 / 1334876815|77523 / 301835|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|2073|3867|1983|596|1.153340%|4|52319|
|HUNSPELL CZECH LUCENE FILTER|10760|1306|2509|3317|6.418840%|5|55596|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **7 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.997139|3863 / 1298544215 (0.000297%)|1709 / 298813 (0.571930%)|0.988580|0.990710|0.990714|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.852770|11239 / 1298544215 (0.000866%)|87986 / 298813 (29.445171%)|0.888009|0.809505|0.818403|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.791794|13950 / 1298544215 (0.001074%)|124426 / 298813 (41.640089%)|0.828709|0.715948|0.735055|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987165|0.994281|0.999997|0.997139|0.999996|0.000004|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.949389|0.705548|0.999991|0.852770|0.999924|0.000076|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.925931|0.583599|0.999989|0.791794|0.999893|0.000107|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988580|0.990710|0.992849|0.981591|0.990716|0.990714|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.888009|0.809505|0.743753|0.679973|0.818437|0.818403|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.828709|0.715948|0.630198|0.557569|0.735100|0.735055|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990708|0.998726|0.999030|0.998878|0.998878|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|0.809467|0.995812|0.952394|0.973619|0.973619|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|0.715897|0.993897|0.944297|0.968463|0.968463|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|297104|3863|1709|1298540352|3863 / 1298544215|1709 / 298813|
|2|HUNSPELL CZECH LUCENE FILTER|PRIMARY_OUTPUT|210827|11239|87986|1298532976|11239 / 1298544215|87986 / 298813|
|3|CZECH LUCENE CZECH STEM FILTER|PRIMARY_OUTPUT|174387|13950|124426|1298530265|13950 / 1298544215|124426 / 298813|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1298544215 (0.000000%)|0 / 298813 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.870432|10028 / 1298544215 (0.000772%)|77431 / 298813 (25.912862%)|0.904004|0.835052|0.841852|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.956666|0.740871|0.999992|0.870432|0.999933|0.000067|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|0.904004|0.835052|0.775874|0.716815|0.841883|0.841852|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|298813|0|0|1298544215|0 / 1298544215|0 / 298813|
|2|HUNSPELL CZECH LUCENE FILTER|ANY_CANDIDATE|221382|10028|77431|1298534187|10028 / 1298544215|77431 / 298813|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|5782 / 1298544215 (0.000445%)|0 / 298813 (0.000000%)|0.984756|0.990418|0.990461|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.870430|13601 / 1298544215 (0.001047%)|77431 / 298813 (25.912862%)|0.893574|0.829463|0.835425|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.981017|1.000000|0.999996|0.999998|0.999996|0.000004|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.942119|0.740871|0.999990|0.870430|0.999930|0.000070|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.984756|0.990418|0.996145|0.981017|0.990463|0.990461|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|0.893574|0.829463|0.773936|0.708617|0.835457|0.835425|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|298813|5782|0|1298538433|5782 / 1298544215|0 / 298813|
|2|HUNSPELL CZECH LUCENE FILTER|ALL_CANDIDATES|221382|13601|77431|1298530614|13601 / 1298544215|77431 / 298813|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|1709|3863|1919|540|1.059488%|4|51543|
|HUNSPELL CZECH LUCENE FILTER|10555|1211|2362|3237|6.351044%|5|54804|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `CS_CZ`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Danish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Danish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `DA_DK` | 4,179 | 32,256 | 8,356 | 23,900 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **32,256**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 137 | 0.425% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 1,127 | 3.494% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 22,586 | 70.021% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 8,405 | 26.057% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 1 | 0.003% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.371% | 99.527% | 98.923% | Full Radixor dictionary patch-command stemmer. |
| Lucene SnowballFilter | 55.509% | 54.159% | 59.371% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 55.509% | 54.159% | 59.371% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `radixor[DANISH]` | 1.143 | 0.017 | 47.8 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Official Snowball direct | `snowballDirect[DANISH]` | 2.168 | 0.058 | 90.7 | 1.896 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[DANISH]` | 2.975 | 0.143 | 124.5 | 2.602 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `DA_DK` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/da_dk/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996066** among 3 deterministic stemmers. The runner-up is `SNOWBALL DANISH LUCENE FILTER` at 0.937969, a difference of 0.058097. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996305** among 3 deterministic stemmers. The runner-up is `SNOWBALL DANISH DIRECT` at 0.938074, a difference of 0.058230. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996066|1165 / 394111186 (0.000296%)|707 / 89895 (0.786473%)|0.988108|0.989614|0.989615|
|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.937969|6507 / 394111186 (0.001651%)|11151 / 89895 (12.404472%)|0.913718|0.899181|0.899475|
|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.937903|6341 / 394111186 (0.001609%)|11163 / 89895 (12.417821%)|0.915090|0.899959|0.900279|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987106|0.992135|0.999997|0.996066|0.999995|0.000005|
|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.923672|0.875955|0.999983|0.937969|0.999955|0.000045|
|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.925464|0.875822|0.999984|0.937903|0.999956|0.000044|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988108|0.989614|0.991125|0.979442|0.989618|0.989615|
|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.913718|0.899181|0.885100|0.816830|0.899498|0.899475|
|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.915090|0.899959|0.885320|0.818114|0.900301|0.900279|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989612|0.998466|0.998719|0.998592|0.998592|
|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.899159|0.994053|0.978603|0.986268|0.986268|
|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.899937|0.994196|0.978579|0.986326|0.986326|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|89188|1165|707|394110021|1165 / 394111186|707 / 89895|
|2|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|78744|6507|11151|394104679|6507 / 394111186|11151 / 89895|
|3|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|78732|6341|11163|394104845|6341 / 394111186|11163 / 89895|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 394111186 (0.000000%)|0 / 89895 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|89895|0|0|394111186|0 / 394111186|0 / 89895|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|1849 / 394111186 (0.000469%)|0 / 89895 (0.000000%)|0.983812|0.989820|0.989869|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.979846|1.000000|0.999995|0.999998|0.999995|0.000005|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.983812|0.989820|0.995903|0.979846|0.989872|0.989869|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|89895|1849|0|394109337|1849 / 394111186|0 / 89895|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|707|1165|684|323|1.150326%|3|28405|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996305|1165 / 392820788 (0.000297%)|663 / 89740 (0.738801%)|0.988190|0.989843|0.989845|
|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.938074|6341 / 392820788 (0.001614%)|11113 / 89740 (12.383552%)|0.915093|0.900096|0.900410|
|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.938074|6341 / 392820788 (0.001614%)|11113 / 89740 (12.383552%)|0.915093|0.900096|0.900410|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987090|0.992612|0.999997|0.996305|0.999995|0.000005|
|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.925372|0.876164|0.999984|0.938074|0.999956|0.000044|
|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.925372|0.876164|0.999984|0.938074|0.999956|0.000044|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988190|0.989843|0.991503|0.979891|0.989847|0.989845|
|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.915093|0.900096|0.885583|0.818341|0.900432|0.900410|
|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.915093|0.900096|0.885583|0.818341|0.900432|0.900410|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989841|0.998463|0.998812|0.998637|0.998637|
|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|0.900074|0.994185|0.978644|0.986354|0.986354|
|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|0.900074|0.994185|0.978644|0.986354|0.986354|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|89077|1165|663|392819623|1165 / 392820788|663 / 89740|
|2|SNOWBALL DANISH DIRECT|PRIMARY_OUTPUT|78627|6341|11113|392814447|6341 / 392820788|11113 / 89740|
|3|SNOWBALL DANISH LUCENE FILTER|PRIMARY_OUTPUT|78627|6341|11113|392814447|6341 / 392820788|11113 / 89740|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 392820788 (0.000000%)|0 / 89740 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|89740|0|0|392820788|0 / 392820788|0 / 89740|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|1849 / 392820788 (0.000471%)|0 / 89740 (0.000000%)|0.983784|0.989803|0.989852|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.979812|1.000000|0.999995|0.999998|0.999995|0.000005|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.983784|0.989803|0.995896|0.979812|0.989855|0.989852|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|89740|1849|0|392818939|1849 / 392820788|0 / 89740|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|663|1165|684|315|1.123676%|3|28351|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `DA_DK`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Dutch Stemmer Benchmarks
This page reports same-language stemming benchmarks for Dutch. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `NL_NL` | 4,992 | 31,466 | 9,981 | 21,485 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **31,466**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 2,107 | 6.696% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 11,484 | 36.497% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 7,732 | 24.573% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 10,127 | 32.184% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 16 | 0.051% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.120% | 98.711% | 100.000% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 46.590% | 22.718% | 97.976% | Benchmark-only Dutch Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Official Snowball direct | 15.954% | 8.992% | 30.939% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene SnowballFilter | 12.620% | 5.441% | 28.073% | Lucene TokenFilter integration path around the Snowball algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `radixor[DUTCH]` | 1.331 | 0.114 | 61.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 22.760 | 1.387 | 1059.3 | 17.105 | Benchmark-only Dutch Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Official Snowball direct | `snowballDirect[DUTCH]` | 4.146 | 0.291 | 193.0 | 3.116 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[DUTCH]` | 7.375 | 0.595 | 343.3 | 5.543 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NL_NL` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/nl_nl/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.988661** among 4 deterministic stemmers. The runner-up is `SNOWBALL DUTCH DIRECT` at 0.727087, a difference of 0.261574. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989040** among 4 deterministic stemmers. The runner-up is `SNOWBALL DUTCH DIRECT` at 0.730495, a difference of 0.258544. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **8 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988661|1214 / 350437960 (0.000346%)|1464 / 64566 (2.267447%)|0.980362|0.979221|0.979219|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.727087|4382 / 350437960 (0.001250%)|35241 / 64566 (54.581359%)|0.735353|0.596807|0.628557|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.643123|1333 / 350437960 (0.000380%)|46084 / 64566 (71.375027%)|0.642512|0.438061|0.516674|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.618497|1588 / 350437960 (0.000453%)|49264 / 64566 (76.300220%)|0.579068|0.375712|0.463333|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.981124|0.977326|0.999997|0.988661|0.999992|0.000008|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.869997|0.454186|0.999987|0.727087|0.999887|0.000113|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.932728|0.286250|0.999996|0.643123|0.999865|0.000135|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.905980|0.236998|0.999995|0.618497|0.999855|0.000145|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.980362|0.979221|0.978083|0.959289|0.979223|0.979219|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.735353|0.596807|0.502190|0.425321|0.628602|0.628557|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.642512|0.438061|0.332316|0.280459|0.516714|0.516674|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.579068|0.375712|0.278062|0.231309|0.463374|0.463333|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.979217|0.997464|0.997003|0.997234|0.997234|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.596756|0.992815|0.917346|0.953590|0.953590|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.438012|0.996932|0.889026|0.939892|0.939892|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.375664|0.995828|0.888410|0.939057|0.939057|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|63102|1214|1464|350436746|1214 / 350437960|1464 / 64566|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|29325|4382|35241|350433578|4382 / 350437960|35241 / 64566|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|18482|1333|46084|350436627|1333 / 350437960|46084 / 64566|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|15302|1588|49264|350436372|1588 / 350437960|49264 / 64566|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 350437960 (0.000000%)|0 / 64566 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.665519|1164 / 350437960 (0.000332%)|43192 / 64566 (66.895889%)|0.690741|0.490770|0.560268|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.948354|0.331041|0.999997|0.665519|0.999873|0.000127|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.690741|0.490770|0.380588|0.325179|0.560307|0.560268|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|64566|0|0|350437960|0 / 350437960|0 / 64566|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|21374|1164|43192|350436796|1164 / 350437960|43192 / 64566|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999996|2651 / 350437960 (0.000756%)|0 / 64566 (0.000000%)|0.968198|0.979884|0.980078|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.665518|1738 / 350437960 (0.000496%)|43192 / 64566 (66.895889%)|0.680640|0.487557|0.553265|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.960561|1.000000|0.999992|0.999996|0.999992|0.000008|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.924801|0.331041|0.999995|0.665518|0.999872|0.000128|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.968198|0.979884|0.991855|0.960561|0.980082|0.980078|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.680640|0.487557|0.379812|0.322364|0.553306|0.553265|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|64566|2651|0|350435309|2651 / 350437960|0 / 64566|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|21374|1738|43192|350436222|1738 / 350437960|43192 / 64566|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL DUTCH LUCENE FILTER|2892|169|405|1254|4.736186%|3|27763|
|Radixor|1464|1214|1437|572|2.160366%|3|27061|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **8 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989040|1214 / 329603856 (0.000368%)|1384 / 63147 (2.191711%)|0.980194|0.979401|0.979398|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.730495|4382 / 329603856 (0.001329%)|34036 / 63147 (53.899631%)|0.738412|0.602463|0.632953|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.645159|1310 / 329603856 (0.000397%)|44814 / 63147 (70.967742%)|0.646808|0.442880|0.520498|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.618546|1544 / 329603856 (0.000468%)|48175 / 63147 (76.290243%)|0.579362|0.375883|0.463566|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.980723|0.978083|0.999996|0.989040|0.999992|0.000008|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.869167|0.461004|0.999987|0.730495|0.999883|0.000117|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.933310|0.290323|0.999996|0.645159|0.999860|0.000140|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.906515|0.237098|0.999995|0.618546|0.999849|0.000151|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.980194|0.979401|0.978610|0.959634|0.979402|0.979398|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.738412|0.602463|0.508789|0.431089|0.633000|0.632953|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.646808|0.442880|0.336718|0.284422|0.520539|0.520498|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.579362|0.375883|0.278182|0.231439|0.463608|0.463566|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.979397|0.997373|0.997139|0.997256|0.997256|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|0.602410|0.992557|0.918059|0.953856|0.953856|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.442829|0.996884|0.889061|0.939890|0.939890|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|0.375834|0.995817|0.887492|0.938539|0.938539|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|61763|1214|1384|329602642|1214 / 329603856|1384 / 63147|
|2|SNOWBALL DUTCH DIRECT|PRIMARY_OUTPUT|29111|4382|34036|329599474|4382 / 329603856|34036 / 63147|
|3|HUNSPELL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|18333|1310|44814|329602546|1310 / 329603856|44814 / 63147|
|4|SNOWBALL DUTCH LUCENE FILTER|PRIMARY_OUTPUT|14972|1544|48175|329602312|1544 / 329603856|48175 / 63147|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 329603856 (0.000000%)|0 / 63147 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.667956|1141 / 329603856 (0.000346%)|41935 / 63147 (66.408539%)|0.695206|0.496187|0.564555|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.948955|0.335915|0.999997|0.667956|0.999869|0.000131|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|0.695206|0.496187|0.385755|0.329953|0.564595|0.564555|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|63147|0|0|329603856|0 / 329603856|0 / 63147|
|2|HUNSPELL DUTCH LUCENE FILTER|ANY_CANDIDATE|21212|1141|41935|329602715|1141 / 329603856|41935 / 63147|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999996|2651 / 329603856 (0.000804%)|0 / 63147 (0.000000%)|0.967506|0.979441|0.979644|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.667955|1712 / 329603856 (0.000519%)|41935 / 63147 (66.408539%)|0.684952|0.492895|0.557477|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.959710|1.000000|0.999992|0.999996|0.999992|0.000008|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.925318|0.335915|0.999995|0.667955|0.999868|0.000132|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.967506|0.979441|0.991674|0.959710|0.979648|0.979644|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|0.684952|0.492895|0.384956|0.327048|0.557519|0.557477|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|63147|2651|0|329601205|2651 / 329603856|0 / 63147|
|2|HUNSPELL DUTCH LUCENE FILTER|ALL_CANDIDATES|21212|1712|41935|329602144|1712 / 329603856|41935 / 63147|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL DUTCH LUCENE FILTER|2879|169|402|1186|4.618740%|3|26896|
|Radixor|1384|1214|1437|549|2.138017%|3|26239|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `NL_NL`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# English Stemmer Benchmarks
This page reports same-language stemming benchmarks for English. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `US_UK` | 396,939 | 1,004,374 | 793,874 | 210,500 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **1,004,374**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 28 | 0.003% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 22,493 | 2.240% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 186,764 | 18.595% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 795,024 | 79.156% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 65 | 0.006% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 97.478% | 97.197% | 97.552% | Full Radixor dictionary patch-command stemmer. |
| Lucene EnglishMinimalStemFilter | 90.981% | 65.189% | 97.820% | Minimal English plural reduction, not a full stemmer. |
| Lucene KStemFilter | 80.076% | 76.608% | 80.996% | Krovetz-style English stemming TokenFilter; broader than minimal suffix reducers. |
| Lucene HunspellStemFilter | 80.243% | 12.750% | 98.139% | Benchmark-only English Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene EnglishPossessiveFilter | 79.032% | 0.003% | 99.987% | Possessive-ending remover only, not a full stemmer. |
| Snowball English / Porter2 | 40.342% | 46.296% | 38.763% | Porter2 rule-based suffix stemmer, distinct from original Porter. |
| Lucene PorterStemFilter | 39.538% | 46.201% | 37.772% | Lucene TokenFilter path for Porter suffix rules; not dictionary-root equivalent. |
| Lucene PorterStemmer direct copy | 39.538% | 46.201% | 37.772% | Direct Porter suffix-rule implementation generated under build for benchmark-only use. |
| OpenNLP PorterStemmer | 39.538% | 46.201% | 37.772% | Apache OpenNLP Porter suffix-rule implementation. |
| Snowball original Porter | 39.529% | 46.179% | 37.766% | Classic Porter rule-based suffix stemmer. |
| Paice/Husk Lancaster | 28.055% | 37.039% | 25.673% | Aggressive Paice/Husk rule stemmer that often produces shorter stems. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `radixorUsUkProfiPreferredStem` | 21.987 | 8.707 | 104.5 | 1.000 | Full dictionary patch-command stemmer using compiled patch commands. |
| Lucene EnglishPossessiveFilter | `luceneEnglishPossessiveFilter` | 24.539 | 1.515 | 116.6 | 1.116 | Possessive-ending remover only; not a full stemmer. |
| Lucene EnglishMinimalStemFilter | `luceneEnglishMinimalStemFilter` | 22.702 | 1.195 | 107.8 | 1.032 | Narrow plural reduction filter; not a full stemmer. |
| Lucene PorterStemmer direct copy | `lucenePorterStemmerCopied` | 24.696 | 13.235 | 117.3 | 1.123 | Benchmark-only generated copy of Lucene package-private Porter implementation. |
| OpenNLP PorterStemmer | `opennlpPorterStemmer` | 23.121 | 12.528 | 109.8 | 1.052 | Apache OpenNLP Porter implementation. |
| Snowball original Porter | `snowballOriginalPorter` | 38.904 | 10.353 | 184.8 | 1.769 | Classic Porter suffix-rule stemmer; historical English baseline, not a dictionary-equivalent stemmer. |
| Lucene PorterStemFilter | `lucenePorterStemFilter` | 37.021 | 1.196 | 175.9 | 1.684 | Lucene TokenFilter integration path for Porter; includes TokenStream overhead. |
| Lucene KStemFilter | `luceneKStemFilter` | 51.640 | 2.591 | 245.3 | 2.349 | Krovetz-style English TokenFilter; broader than minimal suffix filters. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 79.785 | 1.347 | 379.0 | 3.629 | Benchmark-only English Hunspell comparison using the benchmark Hunspell corpus. |
| Snowball English / Porter2 | `snowballEnglishPorter2` | 52.437 | 0.773 | 249.1 | 2.385 | Porter2 suffix-rule stemmer, distinct from original Porter. |
| Paice/Husk Lancaster | `paiceHuskLancaster` | 141.556 | 12.324 | 672.5 | 6.438 | Aggressive rule-based English stemmer. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `US_UK` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/us_uk/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.965159** among 11 deterministic stemmers. The runner-up is `ENGLISH LUCENE PORTER COPIED` at 0.954627, a difference of 0.010533. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.965820** among 11 deterministic stemmers. The runner-up is `ENGLISH LUCENE PORTER COPIED` at 0.954900, a difference of 0.010920. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **15 result rows**, **11 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.965159|1149886 / 184490451771 (0.000623%)|21869 / 313870 (6.967534%)|0.240076|0.332621|0.434052|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.954627|1557406 / 184490451771 (0.000844%)|28480 / 313870 (9.073820%)|0.185679|0.264659|0.375252|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.954537|1566711 / 184490451771 (0.000849%)|28536 / 313870 (9.091662%)|0.184753|0.263477|0.374240|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.954490|1555293 / 184490451771 (0.000843%)|28566 / 313870 (9.101220%)|0.185835|0.264849|0.375363|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.952394|3062661 / 184490451771 (0.001660%)|29879 / 313870 (9.519546%)|0.103643|0.155164|0.277089|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.878441|1368501 / 184490451771 (0.000742%)|76305 / 313870 (24.311020%)|0.176284|0.247472|0.334598|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.718599|1122264 / 184490451771 (0.000608%)|176645 / 313870 (56.279670%)|0.128204|0.174436|0.218251|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.573277|1981986 / 184490451771 (0.001074%)|267868 / 313870 (85.343614%)|0.027298|0.039287|0.057655|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.500008|1115154 / 184490451771 (0.000604%)|313863 / 313870 (99.997770%)|0.000007|0.000010|0.000009|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.202513|0.930325|0.999994|0.965159|0.999994|0.000006|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.154868|0.909262|0.999992|0.954627|0.999991|0.000009|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.154064|0.909083|0.999992|0.954537|0.999991|0.000009|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.155006|0.908988|0.999992|0.954490|0.999991|0.000009|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.084858|0.904805|0.999983|0.952394|0.999983|0.000017|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.147917|0.756890|0.999993|0.878441|0.999992|0.000008|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.108953|0.437203|0.999994|0.718599|0.999993|0.000007|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.022684|0.146564|0.999989|0.573277|0.999988|0.000012|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000006|0.000022|0.999994|0.500008|0.999992|0.000008|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.240076|0.332621|0.541270|0.199487|0.434054|0.434052|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.185679|0.264659|0.460563|0.152511|0.375254|0.375252|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.184753|0.263477|0.459102|0.151727|0.374242|0.374240|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.185835|0.264849|0.460751|0.152637|0.375365|0.375363|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.103643|0.155164|0.308543|0.084107|0.277092|0.277089|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.176284|0.247472|0.415099|0.141208|0.334600|0.334598|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.128204|0.174436|0.272816|0.095552|0.218253|0.218251|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.027298|0.039287|0.070051|0.020037|0.057659|0.057655|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000007|0.000010|0.000015|0.000005|0.000012|0.000009|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.332619|0.994215|0.997770|0.995989|0.995989|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.264656|0.969648|0.997199|0.983231|0.983231|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.263474|0.969037|0.997182|0.982908|0.982908|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.264847|0.969891|0.997193|0.983353|0.983353|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.155162|0.937768|0.996600|0.966289|0.966289|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.247470|0.980687|0.992108|0.986364|0.986364|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.174433|0.995202|0.981174|0.988138|0.988138|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.039284|0.993096|0.963677|0.978166|0.978166|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000007|0.995789|0.958019|0.976539|0.976539|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|292001|1149886|21869|184489301885|1149886 / 184490451771|21869 / 313870|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|285390|1557406|28480|184488894365|1557406 / 184490451771|28480 / 313870|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|285334|1566711|28536|184488885060|1566711 / 184490451771|28536 / 313870|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|285304|1555293|28566|184488896478|1555293 / 184490451771|28566 / 313870|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|283991|3062661|29879|184487389110|3062661 / 184490451771|29879 / 313870|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|237565|1368501|76305|184489083270|1368501 / 184490451771|76305 / 313870|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|137225|1122264|176645|184489329507|1122264 / 184490451771|176645 / 313870|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|46002|1981986|267868|184488469785|1981986 / 184490451771|267868 / 313870|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|7|1115154|313863|184489336617|1115154 / 184490451771|313863 / 313870|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999976|12 / 184490451771 (0.000000%)|15 / 313870 (0.004779%)|0.999960|0.999957|0.999957|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.581603|1978852 / 184490451771 (0.001073%)|262641 / 313870 (83.678274%)|0.030370|0.043712|0.064174|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999962|0.999952|1.000000|0.999976|1.000000|0.000000|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.025235|0.163217|0.999989|0.581603|0.999988|0.000012|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999960|0.999957|0.999954|0.999914|0.999957|0.999957|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.030370|0.043712|0.077961|0.022344|0.064178|0.064174|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|313855|12|15|184490451759|12 / 184490451771|15 / 313870|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|51229|1978852|262641|184488472919|1978852 / 184490451771|262641 / 313870|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999945|11482166 / 184490451771 (0.006224%)|15 / 313870 (0.004779%)|0.033039|0.051834|0.163107|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.581603|2008917 / 184490451771 (0.001089%)|262641 / 313870 (83.678274%)|0.029943|0.043158|0.063704|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.026607|0.999952|0.999938|0.999945|0.999938|0.000062|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.024867|0.163217|0.999989|0.581603|0.999988|0.000012|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.033039|0.051834|0.120237|0.026607|0.163112|0.163107|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.029943|0.043158|0.077254|0.022055|0.063708|0.063704|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|313855|11482166|15|184478969605|11482166 / 184490451771|15 / 313870|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|51229|2008917|262641|184488442854|2008917 / 184490451771|262641 / 313870|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|21854|1149874|10332280|29208|4.808384%|1355|2838145|
|HUNSPELL ENGLISH LUCENE FILTER|5227|3134|26931|6837|1.125545%|4|614296|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **15 result rows**, **11 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.965820|1148489 / 170474840204 (0.000674%)|21319 / 311891 (6.835401%)|0.239424|0.331902|0.433722|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.954900|1552702 / 170474840204 (0.000911%)|28130 / 311891 (9.019177%)|0.185277|0.264166|0.374937|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.954850|1561891 / 170474840204 (0.000916%)|28161 / 311891 (9.029116%)|0.184375|0.263016|0.373964|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.954762|1550615 / 170474840204 (0.000910%)|28216 / 311891 (9.046750%)|0.185431|0.264353|0.375045|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.952710|3045870 / 170474840204 (0.001787%)|29493 / 311891 (9.456188%)|0.103633|0.155157|0.277170|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.880820|1367069 / 170474840204 (0.000802%)|74340 / 311891 (23.835250%)|0.176477|0.247899|0.335789|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.719516|1120871 / 170474840204 (0.000657%)|174959 / 311891 (56.096200%)|0.128139|0.174470|0.218621|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.573619|1978041 / 170474840204 (0.001160%)|265965 / 311891 (85.274984%)|0.027312|0.039323|0.057799|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.500005|1113773 / 170474840204 (0.000653%)|311886 / 311891 (99.998397%)|0.000005|0.000007|0.000005|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.201918|0.931646|0.999993|0.965820|0.999993|0.000007|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.154515|0.909808|0.999991|0.954900|0.999991|0.000009|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.153731|0.909709|0.999991|0.954850|0.999991|0.000009|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.154651|0.909532|0.999991|0.954762|0.999991|0.000009|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.084848|0.905438|0.999982|0.952710|0.999982|0.000018|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.148042|0.761647|0.999992|0.880820|0.999992|0.000008|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.108866|0.439038|0.999993|0.719516|0.999992|0.000008|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.022691|0.147250|0.999988|0.573619|0.999987|0.000013|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000004|0.000016|0.999993|0.500005|0.999992|0.000008|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.239424|0.331902|0.540775|0.198970|0.433723|0.433722|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.185277|0.264166|0.460049|0.152184|0.374939|0.374937|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.184375|0.263016|0.458637|0.151421|0.373966|0.373964|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.185431|0.264353|0.460234|0.152308|0.375047|0.375045|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.103633|0.155157|0.308576|0.084103|0.277173|0.277170|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.176477|0.247899|0.416437|0.141487|0.335791|0.335789|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.128139|0.174470|0.273277|0.095572|0.218624|0.218621|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.027312|0.039323|0.070190|0.020056|0.057804|0.057799|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000005|0.000007|0.000011|0.000004|0.000008|0.000005|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.331900|0.993959|0.997731|0.995842|0.995842|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|0.264164|0.968645|0.997109|0.982671|0.982671|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|0.263014|0.968020|0.997096|0.982343|0.982343|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|0.264351|0.968894|0.997102|0.982795|0.982795|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|0.155154|0.936077|0.996487|0.965338|0.965338|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|0.247897|0.979822|0.991991|0.985869|0.985869|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|0.174467|0.994994|0.980520|0.987704|0.987704|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|0.039320|0.993066|0.962317|0.977450|0.977450|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|0.000004|0.995605|0.956423|0.975621|0.975621|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|290572|1148489|21319|170473691715|1148489 / 170474840204|21319 / 311891|
|2|ENGLISH LUCENE PORTER COPIED|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
|3|ENGLISH LUCENE PORTER FILTER|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
|4|ENGLISH OPENNLP PORTER|PRIMARY_OUTPUT|283761|1552702|28130|170473287502|1552702 / 170474840204|28130 / 311891|
|5|ENGLISH SNOWBALL PORTER2|PRIMARY_OUTPUT|283730|1561891|28161|170473278313|1561891 / 170474840204|28161 / 311891|
|6|ENGLISH SNOWBALL ORIGINAL PORTER|PRIMARY_OUTPUT|283675|1550615|28216|170473289589|1550615 / 170474840204|28216 / 311891|
|7|ENGLISH PAICE HUSK LANCASTER|PRIMARY_OUTPUT|282398|3045870|29493|170471794334|3045870 / 170474840204|29493 / 311891|
|8|ENGLISH LUCENE KSTEM FILTER|PRIMARY_OUTPUT|237551|1367069|74340|170473473135|1367069 / 170474840204|74340 / 311891|
|9|ENGLISH LUCENE MINIMAL FILTER|PRIMARY_OUTPUT|136932|1120871|174959|170473719333|1120871 / 170474840204|174959 / 311891|
|10|HUNSPELL ENGLISH LUCENE FILTER|PRIMARY_OUTPUT|45926|1978041|265965|170472862163|1978041 / 170474840204|265965 / 311891|
|11|ENGLISH LUCENE POSSESSIVE FILTER|PRIMARY_OUTPUT|5|1113773|311886|170473726431|1113773 / 170474840204|311886 / 311891|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 170474840204 (0.000000%)|0 / 311891 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.581994|1974950 / 170474840204 (0.001158%)|260741 / 311891 (83.600040%)|0.030387|0.043756|0.064341|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.025246|0.164000|0.999988|0.581994|0.999987|0.000013|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|0.030387|0.043756|0.078123|0.022367|0.064345|0.064341|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|311891|0|0|170474840204|0 / 170474840204|0 / 311891|
|2|HUNSPELL ENGLISH LUCENE FILTER|ANY_CANDIDATE|51150|1974950|260741|170472865254|1974950 / 170474840204|260741 / 311891|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999966|11470018 / 170474840204 (0.006728%)|0 / 311891 (0.000000%)|0.032872|0.051579|0.162697|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.581994|2004598 / 170474840204 (0.001176%)|260741 / 311891 (83.600040%)|0.029965|0.043208|0.063875|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.026472|1.000000|0.999933|0.999966|0.999933|0.000067|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.024881|0.164000|0.999988|0.581994|0.999987|0.000013|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.032872|0.051579|0.119687|0.026472|0.162702|0.162697|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|0.029965|0.043208|0.077422|0.022081|0.063879|0.063875|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|311891|11470018|0|170463370186|11470018 / 170474840204|0 / 311891|
|2|HUNSPELL ENGLISH LUCENE FILTER|ALL_CANDIDATES|51150|2004598|260741|170472835606|2004598 / 170474840204|260741 / 311891|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|21319|1148489|10321529|28826|4.936720%|1355|2812871|
|HUNSPELL ENGLISH LUCENE FILTER|5224|3091|26557|6786|1.162165%|4|590716|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `US_UK`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Finnish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Finnish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `FI_FI` | 57,027 | 1,865,215 | 110,525 | 1,754,690 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **1,865,215**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 745 | 0.040% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 1,176,003 | 63.049% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 565,585 | 30.323% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 116,946 | 6.270% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 5,936 | 0.318% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 98.661% | 98.803% | 96.408% | Full Radixor dictionary patch-command stemmer. |
| Lucene SnowballFilter | 10.991% | 10.268% | 22.471% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 10.991% | 10.268% | 22.471% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene FinnishLightStemFilter | 4.351% | 4.294% | 5.264% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `finnishRadixor` | 308.076 | 15.529 | 175.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene FinnishLightStemFilter | `finnishLuceneFinnishLightStemFilter` | 175.250 | 46.995 | 99.9 | 0.569 | Light Finnish suffix stemmer. |
| Official Snowball direct | `snowballDirect[FINNISH]` | 264.652 | 63.054 | 150.8 | 0.859 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[FINNISH]` | 374.883 | 238.157 | 213.6 | 1.217 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FI_FI` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/fi_fi/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.984594** among 4 deterministic stemmers. The runner-up is `SNOWBALL FINNISH LUCENE FILTER` at 0.740353, a difference of 0.244242. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.988068** among 4 deterministic stemmers. The runner-up is `SNOWBALL FINNISH DIRECT` at 0.738400, a difference of 0.249668. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.984594|731279 / 1641126814491 (0.000045%)|971268 / 31523695 (3.081073%)|0.975128|0.972893|0.972899|
|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.740353|1922153 / 1641126814491 (0.000117%)|16370057 / 31523695 (51.929372%)|0.758996|0.623613|0.653138|
|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.739729|1544812 / 1641126814491 (0.000094%)|16409363 / 31523695 (52.054060%)|0.769880|0.627374|0.659540|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.695969|2223150 / 1641126814491 (0.000135%)|19168306 / 31523695 (60.806025%)|0.687649|0.536000|0.576338|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.976624|0.969189|1.000000|0.984594|0.999999|0.000001|
|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.887434|0.480706|0.999999|0.740353|0.999989|0.000011|
|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.907269|0.479459|0.999999|0.739729|0.999989|0.000011|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.847505|0.391940|0.999999|0.695969|0.999987|0.000013|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.975128|0.972893|0.970667|0.947216|0.972900|0.972899|
|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.758996|0.623613|0.529216|0.453080|0.653142|0.653138|
|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.769880|0.627374|0.529384|0.457061|0.659544|0.659540|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.687649|0.536000|0.439152|0.366120|0.576343|0.576338|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.972892|0.996085|0.993746|0.994914|0.994914|
|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.623608|0.990718|0.904385|0.945585|0.945585|
|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.627369|0.991872|0.904139|0.945975|0.945975|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.535994|0.988126|0.886473|0.934544|0.934544|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|30552427|731279|971268|1641126083212|731279 / 1641126814491|971268 / 31523695|
|2|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|15153638|1922153|16370057|1641124892338|1922153 / 1641126814491|16370057 / 31523695|
|3|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|15114332|1544812|16409363|1641125269679|1544812 / 1641126814491|16409363 / 31523695|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|12355389|2223150|19168306|1641124591341|2223150 / 1641126814491|19168306 / 31523695|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1641126814491 (0.000000%)|0 / 31523695 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|31523695|0|0|1641126814491|0 / 1641126814491|0 / 31523695|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|1683575 / 1641126814491 (0.000103%)|0 / 31523695 (0.000000%)|0.959025|0.973991|0.974320|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.949301|1.000000|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.959025|0.973991|0.989432|0.949301|0.974321|0.974320|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|31523695|1683575|0|1641125130916|1683575 / 1641126814491|0 / 31523695|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|971268|731279|952296|57328|3.164291%|6|1876272|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988068|730145 / 1543589444152 (0.000047%)|735305 / 30813833 (2.386282%)|0.976268|0.976219|0.976218|
|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.738400|1513705 / 1543589444152 (0.000098%)|16121763 / 30813833 (52.319888%)|0.768117|0.624934|0.657464|
|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.738400|1513705 / 1543589444152 (0.000098%)|16121763 / 30813833 (52.319888%)|0.768117|0.624934|0.657464|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.694529|1806392 / 1543589444152 (0.000117%)|18825444 / 30813833 (61.094133%)|0.697056|0.537492|0.581469|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.976301|0.976137|1.000000|0.988068|0.999999|0.000001|
|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.906595|0.476801|0.999999|0.738400|0.999989|0.000011|
|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.906595|0.476801|0.999999|0.738400|0.999989|0.000011|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.869053|0.389059|0.999999|0.694529|0.999987|0.000013|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.976268|0.976219|0.976170|0.953543|0.976219|0.976218|
|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.768117|0.624934|0.526744|0.454475|0.657469|0.657464|
|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.768117|0.624934|0.526744|0.454475|0.657469|0.657464|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.697056|0.537492|0.437372|0.367514|0.581474|0.581469|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.976218|0.996000|0.996069|0.996035|0.996035|
|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|0.624929|0.991732|0.902933|0.945252|0.945252|
|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|0.624929|0.991732|0.902933|0.945252|0.945252|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.537486|0.989268|0.885294|0.934397|0.934397|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|30078528|730145|735305|1543588714007|730145 / 1543589444152|735305 / 30813833|
|2|SNOWBALL FINNISH DIRECT|PRIMARY_OUTPUT|14692070|1513705|16121763|1543587930447|1513705 / 1543589444152|16121763 / 30813833|
|3|SNOWBALL FINNISH LUCENE FILTER|PRIMARY_OUTPUT|14692070|1513705|16121763|1543587930447|1513705 / 1543589444152|16121763 / 30813833|
|4|FINNISH LUCENE FINNISH LIGHT STEM FILTER|PRIMARY_OUTPUT|11988389|1806392|18825444|1543587637760|1806392 / 1543589444152|18825444 / 30813833|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 1543589444152 (0.000000%)|0 / 30813833 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|30813833|0|0|1543589444152|0 / 1543589444152|0 / 30813833|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|1653320 / 1543589444152 (0.000107%)|0 / 30813833 (0.000000%)|0.958843|0.973873|0.974205|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.949077|1.000000|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.958843|0.973873|0.989383|0.949077|0.974206|0.974205|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|30813833|1653320|0|1543587790832|1653320 / 1543589444152|0 / 30813833|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|735305|730145|923175|44331|2.523029%|6|1805864|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `FI_FI`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# French Stemmer Benchmarks
This page reports same-language stemming benchmarks for French. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `FR_FR` | 59,240 | 474,110 | 108,141 | 365,969 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **474,110**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 5,370 | 1.133% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 185,263 | 39.076% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 153,886 | 32.458% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 116,519 | 24.576% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 13,072 | 2.757% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 94.831% | 94.859% | 94.734% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 68.923% | 63.617% | 86.876% | Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene FrenchMinimalStemFilter | 11.472% | 6.236% | 29.192% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Lucene SnowballFilter | 8.551% | 5.183% | 19.952% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 8.462% | 5.067% | 19.952% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene FrenchLightStemFilter | 6.377% | 3.965% | 14.540% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `frenchRadixor` | 47.033 | 4.146 | 128.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 1664.935 | 65.928 | 4549.4 | 35.399 | Benchmark-only French Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene FrenchMinimalStemFilter | `frenchLuceneFrenchMinimalStemFilter` | 19.234 | 2.098 | 52.6 | 0.409 | Minimal French suffix reducer; narrow baseline. |
| Lucene FrenchLightStemFilter | `frenchLuceneFrenchLightStemFilter` | 30.560 | 3.680 | 83.5 | 0.650 | Light French suffix stemmer. |
| Official Snowball direct | `snowballDirect[FRENCH]` | 111.057 | 8.172 | 303.5 | 2.361 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[FRENCH]` | 123.648 | 3.500 | 337.9 | 2.629 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FR_FR` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/fr_fr/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.956992** among 6 deterministic stemmers. The runner-up is `SNOWBALL FRENCH DIRECT` at 0.845262, a difference of 0.111731. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.957224** among 6 deterministic stemmers. The runner-up is `SNOWBALL FRENCH DIRECT` at 0.845414, a difference of 0.111810. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **10 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.956992|318767 / 90396104830 (0.000353%)|469160 / 5454615 (8.601157%)|0.934603|0.926765|0.926851|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.845262|1654723 / 90396104830 (0.001831%)|1687975 / 5454615 (30.945814%)|0.693926|0.692653|0.692638|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.844999|1661388 / 90396104830 (0.001838%)|1690838 / 5454615 (30.998301%)|0.693010|0.691885|0.691869|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.813742|776728 / 90396104830 (0.000859%)|2031881 / 5454615 (37.250677%)|0.769069|0.709075|0.715131|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.518587|276403 / 90396104830 (0.000306%)|5251833 / 5454615 (96.282377%)|0.137547|0.068348|0.125415|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.516830|160438 / 90396104830 (0.000177%)|5271003 / 5454615 (96.633823%)|0.134400|0.063329|0.134021|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.939903|0.913988|0.999996|0.956992|0.999991|0.000009|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.694777|0.690542|0.999982|0.845262|0.999963|0.000037|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693763|0.690017|0.999982|0.844999|0.999963|0.000037|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.815041|0.627493|0.999991|0.813742|0.999969|0.000031|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.423181|0.037176|0.999997|0.518587|0.999939|0.000061|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.533678|0.033662|0.999998|0.516830|0.999940|0.000060|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.934603|0.926765|0.919056|0.863524|0.926855|0.926851|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.693926|0.692653|0.691385|0.529816|0.692656|0.692638|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693010|0.691885|0.690763|0.528917|0.691887|0.691869|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.769069|0.709075|0.657765|0.549277|0.715145|0.715131|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.137547|0.068348|0.045472|0.035383|0.125428|0.125415|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.134400|0.063329|0.041424|0.032700|0.134032|0.134021|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.926760|0.988772|0.985214|0.986990|0.986990|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.692635|0.959459|0.944948|0.952148|0.952148|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.691866|0.958698|0.944715|0.951655|0.951655|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.709060|0.978337|0.913706|0.944918|0.944918|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.068339|0.974110|0.812376|0.885922|0.885922|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.063322|0.984019|0.810979|0.889158|0.889158|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|4985455|318767|469160|90395786063|318767 / 90396104830|469160 / 5454615|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|3766640|1654723|1687975|90394450107|1654723 / 90396104830|1687975 / 5454615|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3763777|1661388|1690838|90394443442|1661388 / 90396104830|1690838 / 5454615|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3422734|776728|2031881|90395328102|776728 / 90396104830|2031881 / 5454615|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|202782|276403|5251833|90395828427|276403 / 90396104830|5251833 / 5454615|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|183612|160438|5271003|90395944392|160438 / 90396104830|5271003 / 5454615|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999979|12 / 90396104830 (0.000000%)|232 / 5454615 (0.004253%)|0.999990|0.999978|0.999978|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830964|745831 / 90396104830 (0.000825%)|1844003 / 5454615 (33.806291%)|0.789019|0.736029|0.740670|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999998|0.999957|1.000000|0.999979|1.000000|0.000000|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.828798|0.661937|0.999992|0.830964|0.999971|0.000029|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999990|0.999978|0.999966|0.999955|0.999978|0.999978|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.789019|0.736029|0.689709|0.582315|0.740684|0.740670|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|5454383|12|232|90396104818|12 / 90396104830|232 / 5454615|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|3610612|745831|1844003|90395358999|745831 / 90396104830|1844003 / 5454615|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999973|1056255 / 90396104830 (0.001168%)|232 / 5454615 (0.004253%)|0.865853|0.911704|0.915270|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.830963|1043199 / 90396104830 (0.001154%)|1844003 / 5454615 (33.806291%)|0.750028|0.714377|0.716613|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.837765|0.999957|0.999988|0.999973|0.999988|0.000012|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.775840|0.661937|0.999988|0.830963|0.999968|0.000032|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.865853|0.911704|0.962682|0.837735|0.915275|0.915270|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.750028|0.714377|0.681961|0.555666|0.716629|0.716613|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|5454383|1056255|232|90395048575|1056255 / 90396104830|232 / 5454615|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|3610612|1043199|1844003|90395061631|1043199 / 90396104830|1844003 / 5454615|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|468928|318755|737488|43040|10.122057%|56|477024|
|HUNSPELL FRENCH LUCENE FILTER|187878|30897|266471|13511|3.177489%|4|439015|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **10 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.957224|315266 / 88712126506 (0.000355%)|465436 / 5440559 (8.554930%)|0.935099|0.927248|0.927334|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.845414|1646111 / 88712126506 (0.001856%)|1681970 / 5440559 (30.915389%)|0.694508|0.693130|0.693115|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.845163|1641925 / 88712126506 (0.001851%)|1684703 / 5440559 (30.965623%)|0.694714|0.693068|0.693055|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.813617|763305 / 88712126506 (0.000860%)|2028011 / 5440559 (37.275784%)|0.770537|0.709734|0.715938|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.518442|262689 / 88712126506 (0.000296%)|5239869 / 5440559 (96.311225%)|0.137571|0.067985|0.126383|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.516697|147476 / 88712126506 (0.000166%)|5258873 / 5440559 (96.660527%)|0.134439|0.062979|0.135757|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.940408|0.914451|0.999996|0.957224|0.999991|0.000009|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.695430|0.690846|0.999981|0.845414|0.999962|0.000038|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.695815|0.690344|0.999981|0.845163|0.999963|0.000037|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.817210|0.627242|0.999991|0.813617|0.999969|0.000031|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.433101|0.036888|0.999997|0.518442|0.999938|0.000062|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.551965|0.033395|0.999998|0.516697|0.999939|0.000061|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.935099|0.927248|0.919527|0.864363|0.927338|0.927334|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.694508|0.693130|0.691758|0.530374|0.693134|0.693115|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.694714|0.693068|0.691431|0.530302|0.693074|0.693055|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.770537|0.709734|0.657826|0.550068|0.715953|0.715938|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.137571|0.067985|0.045148|0.035189|0.126397|0.126383|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.134439|0.062979|0.041121|0.032513|0.135767|0.135757|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.927243|0.988916|0.985550|0.987230|0.987230|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|0.693112|0.959521|0.944537|0.951970|0.951970|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.693050|0.959566|0.944385|0.951915|0.951915|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|0.709719|0.979328|0.913162|0.945088|0.945088|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.067976|0.975086|0.811144|0.885591|0.885591|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.062973|0.985086|0.809774|0.888868|0.888868|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|4975123|315266|465436|88711811240|315266 / 88712126506|465436 / 5440559|
|2|SNOWBALL FRENCH DIRECT|PRIMARY_OUTPUT|3758589|1646111|1681970|88710480395|1646111 / 88712126506|1681970 / 5440559|
|3|SNOWBALL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3755856|1641925|1684703|88710484581|1641925 / 88712126506|1684703 / 5440559|
|4|HUNSPELL FRENCH LUCENE FILTER|PRIMARY_OUTPUT|3412548|763305|2028011|88711363201|763305 / 88712126506|2028011 / 5440559|
|5|FRENCH LUCENE FRENCH LIGHT STEM FILTER|PRIMARY_OUTPUT|200690|262689|5239869|88711863817|262689 / 88712126506|5239869 / 5440559|
|6|FRENCH LUCENE FRENCH MINIMAL STEM FILTER|PRIMARY_OUTPUT|181686|147476|5258873|88711979030|147476 / 88712126506|5258873 / 5440559|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 88712126506 (0.000000%)|0 / 5440559 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830852|733584 / 88712126506 (0.000827%)|1840476 / 5440559 (33.828803%)|0.790351|0.736648|0.741404|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.830724|0.661712|0.999992|0.830852|0.999971|0.000029|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|0.790351|0.736648|0.689779|0.583090|0.741418|0.741404|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|5440559|0|0|88712126506|0 / 88712126506|0 / 5440559|
|2|HUNSPELL FRENCH LUCENE FILTER|ANY_CANDIDATE|3600083|733584|1840476|88711392922|733584 / 88712126506|1840476 / 5440559|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999995|938985 / 88712126506 (0.001058%)|0 / 5440559 (0.000000%)|0.878679|0.920560|0.923474|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.830850|1027635 / 88712126506 (0.001158%)|1840476 / 5440559 (33.828803%)|0.751538|0.715134|0.717460|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.852813|1.000000|0.999989|0.999995|0.999989|0.000011|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.777939|0.661712|0.999988|0.830850|0.999968|0.000032|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.878679|0.920560|0.966634|0.852813|0.923479|0.923474|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|0.751538|0.715134|0.682093|0.556582|0.717476|0.717460|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|5440559|938985|0|88711187521|938985 / 88712126506|0 / 5440559|
|2|HUNSPELL FRENCH LUCENE FILTER|ALL_CANDIDATES|3600083|1027635|1840476|88711098871|1027635 / 88712126506|1840476 / 5440559|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|465436|315266|623719|41130|9.764239%|56|468574|
|HUNSPELL FRENCH LUCENE FILTER|187535|29721|264330|13437|3.189936%|4|434961|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `FR_FR`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# German Stemmer Benchmarks
This page reports same-language stemming benchmarks for German. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `DE_DE` | 39,315 | 213,440 | 73,799 | 139,641 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **213,440**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 3,627 | 1.699% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 48,605 | 22.772% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 80,443 | 37.689% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 75,717 | 35.475% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 5,048 | 2.365% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 92.725% | 92.847% | 92.396% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 47.064% | 29.661% | 93.678% | Benchmark-only German Hunspell dictionary compared via Lucene HunspellStemFilter. |
| CISTEM (German) | 24.675% | 23.724% | 27.222% | Benchmark-only CISTEM implementation. |
| Lucene GermanLightStemFilter | 37.434% | 35.465% | 42.707% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Lucene GermanMinimalStemFilter | 27.640% | 24.951% | 34.844% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Lucene SnowballFilter | 30.956% | 28.853% | 36.589% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 30.481% | 29.027% | 34.376% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene GermanStemFilter | 21.559% | 19.312% | 27.576% | German Lucene stemming TokenFilter; broader than minimal/light variants. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `germanRadixor` | 41.166 | 2.396 | 294.8 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| CISTEM | `germanCistem` | 248.392 | 12.294 | 1778.8 | 6.034 | Benchmark-only CISTEM implementation. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 281.322 | 3.411 | 2014.6 | 6.834 | Benchmark-only German Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene GermanMinimalStemFilter | `germanLuceneGermanMinimalStemFilter` | 23.562 | 0.969 | 168.7 | 0.572 | Minimal German suffix reduction; narrow baseline. |
| Lucene GermanLightStemFilter | `germanLuceneGermanLightStemFilter` | 24.410 | 1.034 | 174.8 | 0.593 | Light German suffix stemmer; narrower than a dictionary stemmer. |
| Lucene GermanStemFilter | `germanLuceneGermanStemFilter` | 71.039 | 4.443 | 508.7 | 1.726 | Older German stemming TokenFilter with normalization requirements. |
| Lucene SnowballFilter | `luceneSnowballFilter[GERMAN]` | 105.771 | 9.617 | 757.4 | 2.569 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
| Official Snowball direct | `snowballDirect[GERMAN]` | 100.688 | 9.018 | 721.0 | 2.446 | Official Snowball generated Java stemmer; direct API. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `DE_DE` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/de_de/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.907901** among 8 deterministic stemmers. The runner-up is `GERMAN CISTEM` at 0.880770, a difference of 0.027131. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.966157** among 8 deterministic stemmers. The runner-up is `GERMAN CISTEM` at 0.915288, a difference of 0.050869. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **12 result rows**, **8 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.907901|98192 / 44095245979 (0.000223%)|254903 / 1383872 (18.419550%)|0.897073|0.864768|0.866326|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.880770|477122 / 44095245979 (0.001082%)|329983 / 1383872 (23.844908%)|0.701852|0.723109|0.724023|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.778614|190680 / 44095245979 (0.000432%)|612734 / 1383872 (44.276783%)|0.737064|0.657494|0.668394|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.771357|295701 / 44095245979 (0.000671%)|632816 / 1383872 (45.727929%)|0.674089|0.617993|0.624014|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.756258|205740 / 44095245979 (0.000467%)|674609 / 1383872 (48.747933%)|0.703092|0.617052|0.630292|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.723772|331871 / 44095245979 (0.000753%)|764518 / 1383872 (55.244849%)|0.596821|0.530474|0.539809|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.641579|203883 / 44095245979 (0.000462%)|992010 / 1383872 (71.683653%)|0.520145|0.395897|0.431563|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.598139|110840 / 44095245979 (0.000251%)|1112246 / 1383872 (80.372029%)|0.466113|0.307558|0.373350|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.919984|0.815804|0.999998|0.907901|0.999992|0.000008|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.688361|0.761551|0.999989|0.880770|0.999982|0.000018|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.801750|0.557232|0.999996|0.778614|0.999982|0.000018|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.717508|0.542721|0.999993|0.771357|0.999979|0.000021|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.775148|0.512521|0.999995|0.756258|0.999980|0.000020|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.651112|0.447552|0.999992|0.723772|0.999975|0.000025|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.657768|0.283163|0.999995|0.641579|0.999973|0.000027|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.710196|0.196280|0.999997|0.598139|0.999972|0.000028|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.897073|0.864768|0.834709|0.761755|0.866330|0.866326|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.701852|0.723109|0.745694|0.566304|0.724032|0.724023|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.737064|0.657494|0.593429|0.489751|0.668402|0.668394|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.674089|0.617993|0.570517|0.447171|0.624024|0.624014|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.703092|0.617052|0.549774|0.446186|0.630301|0.630292|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.596821|0.530474|0.477402|0.360983|0.539820|0.539809|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.520145|0.395897|0.319562|0.246803|0.431574|0.431563|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.466113|0.307558|0.229493|0.181725|0.373359|0.373350|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.864764|0.989946|0.975085|0.982460|0.982460|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.723100|0.974048|0.975147|0.974597|0.974597|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.657485|0.983725|0.949324|0.966218|0.966218|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.617983|0.975845|0.942925|0.959102|0.959102|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617043|0.980753|0.936533|0.958133|0.958133|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.530462|0.975550|0.942890|0.958942|0.958942|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.395885|0.980463|0.886873|0.931322|0.931322|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.307549|0.983615|0.896264|0.937910|0.937910|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|1128969|98192|254903|44095147787|98192 / 44095245979|254903 / 1383872|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|1053889|477122|329983|44094768857|477122 / 44095245979|329983 / 1383872|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|771138|190680|612734|44095055299|190680 / 44095245979|612734 / 1383872|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|751056|295701|632816|44094950278|295701 / 44095245979|632816 / 1383872|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|709263|205740|674609|44095040239|205740 / 44095245979|674609 / 1383872|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|619354|331871|764518|44094914108|331871 / 44095245979|764518 / 1383872|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|391862|203883|992010|44095042096|203883 / 44095245979|992010 / 1383872|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|271626|110840|1112246|44095135139|110840 / 44095245979|1112246 / 1383872|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.959835|1375 / 44095245979 (0.000003%)|111167 / 1383872 (8.033041%)|0.981996|0.957658|0.958475|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.647474|158403 / 44095245979 (0.000359%)|975697 / 1383872 (70.504859%)|0.559116|0.418544|0.460956|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.998921|0.919670|1.000000|0.959835|0.999997|0.000003|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.720422|0.294951|0.999996|0.647474|0.999974|0.000026|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.981996|0.957658|0.934498|0.918757|0.958476|0.958475|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.559116|0.418544|0.334456|0.264658|0.460966|0.460956|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1272705|1375|111167|44095244604|1375 / 44095245979|111167 / 1383872|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|408175|158403|975697|44095087576|158403 / 44095245979|975697 / 1383872|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.959832|244817 / 44095245979 (0.000555%)|111167 / 1383872 (8.033041%)|0.853711|0.877306|0.878234|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.647473|242551 / 44095245979 (0.000550%)|975697 / 1383872 (70.504859%)|0.511911|0.401234|0.430118|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.838673|0.919670|0.999994|0.959832|0.999992|0.000008|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.627261|0.294951|0.999994|0.647473|0.999972|0.000028|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.853711|0.877306|0.902242|0.781429|0.878238|0.878234|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.511911|0.401234|0.329907|0.250965|0.430130|0.430118|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|1272705|244817|111167|44095001162|244817 / 44095245979|111167 / 1383872|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|408175|242551|975697|44095003428|242551 / 44095245979|975697 / 1383872|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|143736|96817|146625|48574|16.356314%|8|361016|
|HUNSPELL GERMAN LUCENE FILTER|16313|45480|38668|7891|2.657135%|3|305052|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **12 result rows**, **8 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.966157|47898 / 11263756342 (0.000425%)|59114 / 873411 (6.768177%)|0.941996|0.938343|0.938358|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.915288|156784 / 11263756342 (0.001392%)|147964 / 873411 (16.940936%)|0.823934|0.826418|0.826415|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.795926|87697 / 11263756342 (0.000779%)|356475 / 873411 (40.814118%)|0.785153|0.699487|0.711329|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.775641|77653 / 11263756342 (0.000689%)|391910 / 873411 (44.871200%)|0.774111|0.672222|0.688986|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.769953|55477 / 11263756342 (0.000493%)|401846 / 873411 (46.008809%)|0.790797|0.673446|0.695023|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.716810|78723 / 11263756342 (0.000699%)|494677 / 873411 (56.637368%)|0.700519|0.569153|0.599149|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.659196|84679 / 11263756342 (0.000752%)|595318 / 873411 (68.160122%)|0.598178|0.449922|0.494019|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.575691|21214 / 11263756342 (0.000188%)|741190 / 873411 (84.861537%)|0.444545|0.257528|0.361168|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.944446|0.932318|0.999996|0.966157|0.999991|0.000009|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.822287|0.830591|0.999986|0.915288|0.999973|0.000027|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.854958|0.591859|0.999992|0.795926|0.999961|0.000039|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.861124|0.551288|0.999993|0.775641|0.999958|0.000042|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.894739|0.539912|0.999995|0.769953|0.999959|0.000041|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.827912|0.433626|0.999993|0.716810|0.999949|0.000051|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.766578|0.318399|0.999992|0.659196|0.999940|0.000060|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.861739|0.151385|0.999998|0.575691|0.999932|0.000068|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.941996|0.938343|0.934719|0.883848|0.938363|0.938358|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.823934|0.826418|0.828917|0.704184|0.826428|0.826415|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.785153|0.699487|0.630675|0.537854|0.711347|0.711329|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.774111|0.672222|0.594035|0.506276|0.689005|0.688986|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.790797|0.673446|0.586424|0.507666|0.695040|0.695023|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.700519|0.569153|0.479277|0.397774|0.599170|0.599149|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.598178|0.449922|0.360559|0.290258|0.494042|0.494019|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.444545|0.257528|0.181270|0.147795|0.361184|0.361168|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.938338|0.994062|0.990664|0.992360|0.992360|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|0.826404|0.985936|0.973570|0.979714|0.979714|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|0.699468|0.988418|0.932452|0.959619|0.959619|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.672202|0.989021|0.919542|0.953017|0.953017|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.673427|0.991320|0.915070|0.951670|0.951670|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|0.569130|0.988584|0.918718|0.952371|0.952371|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|0.449897|0.988041|0.865581|0.922766|0.922766|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.257511|0.992643|0.854403|0.918349|0.918349|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|814297|47898|59114|11263708444|47898 / 11263756342|59114 / 873411|
|2|GERMAN CISTEM|PRIMARY_OUTPUT|725447|156784|147964|11263599558|156784 / 11263756342|147964 / 873411|
|3|SNOWBALL GERMAN DIRECT|PRIMARY_OUTPUT|516936|87697|356475|11263668645|87697 / 11263756342|356475 / 873411|
|4|SNOWBALL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|481501|77653|391910|11263678689|77653 / 11263756342|391910 / 873411|
|5|GERMAN LUCENE GERMAN LIGHT STEM FILTER|PRIMARY_OUTPUT|471565|55477|401846|11263700865|55477 / 11263756342|401846 / 873411|
|6|GERMAN LUCENE GERMAN STEM FILTER|PRIMARY_OUTPUT|378734|78723|494677|11263677619|78723 / 11263756342|494677 / 873411|
|7|HUNSPELL GERMAN LUCENE FILTER|PRIMARY_OUTPUT|278093|84679|595318|11263671663|84679 / 11263756342|595318 / 873411|
|8|GERMAN LUCENE GERMAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|132221|21214|741190|11263735128|21214 / 11263756342|741190 / 873411|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 11263756342 (0.000000%)|0 / 873411 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.665363|60996 / 11263756342 (0.000542%)|584547 / 873411 (66.926911%)|0.635466|0.472281|0.522540|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.825656|0.330731|0.999995|0.665363|0.999943|0.000057|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|0.635466|0.472281|0.375782|0.309142|0.522561|0.522540|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|873411|0|0|11263756342|0 / 11263756342|0 / 873411|
|2|HUNSPELL GERMAN LUCENE FILTER|ANY_CANDIDATE|288864|60996|584547|11263695346|60996 / 11263756342|584547 / 873411|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999996|97544 / 11263756342 (0.000866%)|0 / 873411 (0.000000%)|0.917983|0.947112|0.948436|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.665361|96545 / 11263756342 (0.000857%)|584547 / 873411 (66.926911%)|0.598050|0.458944|0.497855|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.899538|1.000000|0.999991|0.999996|0.999991|0.000009|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.749500|0.330731|0.999991|0.665361|0.999940|0.000060|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.917983|0.947112|0.978152|0.899538|0.948440|0.948436|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|0.598050|0.458944|0.372338|0.297811|0.497878|0.497855|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|873411|97544|0|11263658798|97544 / 11263756342|0 / 873411|
|2|HUNSPELL GERMAN LUCENE FILTER|ALL_CANDIDATES|288864|96545|584547|11263659797|96545 / 11263756342|584547 / 873411|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|59114|47898|49646|14978|9.978814%|8|167157|
|HUNSPELL GERMAN LUCENE FILTER|10771|23683|11866|4989|3.323828%|3|155207|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `DE_DE`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Hungarian Stemmer Benchmarks
This page reports same-language stemming benchmarks for Hungarian. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `HU_HU` | 19,406 | 935,713 | 38,775 | 896,938 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **935,713**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 15 | 0.002% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 149,173 | 15.942% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 746,296 | 79.757% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 40,125 | 4.288% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 104 | 0.011% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.222% | 99.537% | 91.948% | Full Radixor dictionary patch-command stemmer. |
| Lucene SnowballFilter | 66.445% | 66.938% | 55.043% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 66.445% | 66.938% | 55.043% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene HungarianLightStemFilter | 14.748% | 14.777% | 14.086% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `hungarianRadixor` | 62.232 | 6.412 | 69.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HungarianLightStemFilter | `hungarianLuceneHungarianLightStemFilter` | 92.813 | 6.929 | 103.5 | 1.491 | Light Hungarian suffix stemmer. |
| Official Snowball direct | `snowballDirect[HUNGARIAN]` | 157.765 | 13.202 | 175.9 | 2.535 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[HUNGARIAN]` | 188.863 | 15.880 | 210.6 | 3.035 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `HU_HU` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/hu_hu/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.995491** among 4 deterministic stemmers. The runner-up is `SNOWBALL HUNGARIAN LUCENE FILTER` at 0.822606, a difference of 0.172885. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996163** among 4 deterministic stemmers. The runner-up is `SNOWBALL HUNGARIAN DIRECT` at 0.821708, a difference of 0.174455. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.995491|272900 / 419820542893 (0.000065%)|199837 / 22162103 (0.901706%)|0.988376|0.989352|0.989353|
|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.822606|1792049 / 419820542893 (0.000427%)|7862745 / 22162103 (35.478334%)|0.826288|0.747610|0.757196|
|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.822348|1506056 / 419820542893 (0.000359%)|7874191 / 22162103 (35.529981%)|0.837137|0.752866|0.763681|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.816668|4132555 / 419820542893 (0.000984%)|8125833 / 22162103 (36.665442%)|0.740018|0.696055|0.699478|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987727|0.990983|0.999999|0.995491|0.999999|0.000001|
|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.888633|0.645217|0.999996|0.822606|0.999977|0.000023|
|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.904644|0.644700|0.999996|0.822348|0.999978|0.000022|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.772547|0.633346|0.999990|0.816668|0.999971|0.000029|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988376|0.989352|0.990330|0.978929|0.989353|0.989353|
|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.826288|0.747610|0.682613|0.596947|0.757206|0.757196|
|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.837137|0.752866|0.684009|0.603677|0.763691|0.763681|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.740018|0.696055|0.657023|0.533807|0.699492|0.699478|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989352|0.998036|0.997809|0.997922|0.997922|
|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.747599|0.990687|0.924490|0.956445|0.956445|
|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.752855|0.991948|0.924304|0.956932|0.956932|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.696040|0.982615|0.926772|0.953877|0.953877|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|21962266|272900|199837|419820269993|272900 / 419820542893|199837 / 22162103|
|2|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|14299358|1792049|7862745|419818750844|1792049 / 419820542893|7862745 / 22162103|
|3|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|14287912|1506056|7874191|419819036837|1506056 / 419820542893|7874191 / 22162103|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|14036270|4132555|8125833|419816410338|4132555 / 419820542893|8125833 / 22162103|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 419820542893 (0.000000%)|0 / 22162103 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|22162103|0|0|419820542893|0 / 419820542893|0 / 22162103|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|460158 / 419820542893 (0.000110%)|0 / 22162103 (0.000000%)|0.983661|0.989725|0.989777|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.979659|1.000000|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.983661|0.989725|0.995865|0.979659|0.989777|0.989777|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|22162103|460158|0|419820082735|460158 / 419820542893|0 / 22162103|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|199837|272900|187258|12320|1.344473%|5|929326|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996163|272775 / 385870694917 (0.000071%)|164277 / 21411411 (0.767240%)|0.988321|0.989820|0.989822|
|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.821708|1496670 / 385870694917 (0.000388%)|7634885 / 21411411 (35.658019%)|0.834899|0.751079|0.761809|
|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.821708|1496670 / 385870694917 (0.000388%)|7634885 / 21411411 (35.658019%)|0.834899|0.751079|0.761809|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.815077|3639046 / 385870694917 (0.000943%)|7918708 / 21411411 (36.983588%)|0.750108|0.700135|0.704477|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987325|0.992328|0.999999|0.996163|0.999999|0.000001|
|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.902007|0.643420|0.999996|0.821708|0.999976|0.000024|
|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.902007|0.643420|0.999996|0.821708|0.999976|0.000024|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.787585|0.630164|0.999991|0.815077|0.999970|0.000030|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988321|0.989820|0.991323|0.979845|0.989823|0.989822|
|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.834899|0.751079|0.682555|0.601383|0.761820|0.761809|
|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834899|0.751079|0.682555|0.601383|0.761820|0.761809|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.750108|0.700135|0.656404|0.538621|0.704491|0.704477|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989819|0.997945|0.998273|0.998109|0.998109|
|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|0.751068|0.991610|0.923288|0.956230|0.956230|
|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|0.751068|0.991610|0.923288|0.956230|0.956230|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.700120|0.983687|0.925487|0.953700|0.953700|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|21247134|272775|164277|385870422142|272775 / 385870694917|164277 / 21411411|
|2|SNOWBALL HUNGARIAN DIRECT|PRIMARY_OUTPUT|13776526|1496670|7634885|385869198247|1496670 / 385870694917|7634885 / 21411411|
|3|SNOWBALL HUNGARIAN LUCENE FILTER|PRIMARY_OUTPUT|13776526|1496670|7634885|385869198247|1496670 / 385870694917|7634885 / 21411411|
|4|HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|13492703|3639046|7918708|385867055871|3639046 / 385870694917|7918708 / 21411411|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 385870694917 (0.000000%)|0 / 21411411 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|21411411|0|0|385870694917|0 / 385870694917|0 / 21411411|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|458462 / 385870694917 (0.000119%)|0 / 21411411 (0.000000%)|0.983159|0.989407|0.989462|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.979037|1.000000|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.983159|0.989407|0.995736|0.979037|0.989463|0.989462|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|21411411|458462|0|385870236455|458462 / 385870694917|0 / 21411411|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|164277|272775|185687|11153|1.269532%|5|890245|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `HU_HU`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Language Benchmark Pages
This section splits Radixor stemmer benchmark results by language. Each language page preserves the existing exact-root accuracy and runtime-performance results and adds pairwise stemming-quality tables for both dictionary-processing modes.
## Reference Pages
| Page | Purpose |
| --- | --- |
| [Methodology](../reference/methodology.md) | Workload design, normalization, speed metrics, and exact-root quality metrics. Pairwise quality definitions are also reproduced on every language page. |
| [Corpora](../reference/corpora.md) | Dictionary sizes and changed-token timing workloads. |
| [Environment and reports](../reference/environment.md) | Hardware, JVM, JMH settings, report files, and badge policy. |
| [English dictionary coverage](../reference/english-coverage.md) | Quality/speed operating curve for contracted Radixor tries built from 100% down to 10% of English dictionary rows. |
| [Candidate evaluation](../reference/candidates.md) | Included and skipped stemmer candidates. |
## Languages
| Language | Resource | Benchmark page |
| --- | --- | --- |
| Czech | `CS_CZ` | [Czech](czech.md) |
| Danish | `DA_DK` | [Danish](danish.md) |
| Dutch | `NL_NL` | [Dutch](dutch.md) |
| English | `US_UK` | [English](english.md) |
| Finnish | `FI_FI` | [Finnish](finnish.md) |
| French | `FR_FR` | [French](french.md) |
| German | `DE_DE` | [German](german.md) |
| Hungarian | `HU_HU` | [Hungarian](hungarian.md) |
| Italian | `IT_IT` | [Italian](italian.md) |
| Norwegian Bokmal | `NB_NO` | [Norwegian Bokmal](norwegian-bokmal.md) |
| Norwegian Nynorsk | `NN_NO` | [Norwegian Nynorsk](norwegian-nynorsk.md) |
| Persian | `FA_IR` | [Persian](persian.md) |
| Polish | `PL_PL` | [Polish](polish.md) |
| Portuguese | `PT_PT` | [Portuguese](portuguese.md) |
| Russian | `RU_RU` | [Russian](russian.md) |
| Spanish | `ES_ES` | [Spanish](spanish.md) |
| Swedish | `SV_SE` | [Swedish](swedish.md) |
| Ukrainian | `UK_UA` | [Ukrainian](ukrainian.md) |
| Yiddish | `YI` | [Yiddish](yiddish.md) |
## Methodology Notes
- Speed benchmarks process only changed dictionary tokens where the surface form differs from the expected root.
- Accuracy benchmarks process the complete dictionary and report `All exact`, `Changed exact`, and `Root preserved`.
- Radixor speed must be interpreted together with exact-root quality. A slower Radixor row must not be read as a simple performance weakness when Radixor is also the row with accuracy close to 100% and competing stemmers are much lower. Many fast light, minimal, possessive, or aggressive rule-based stemmers are fast because they do much less linguistic work. The measured Radixor cost buys dictionary-trained precision, and that precision is what improves search quality when queries and indexed text are reduced to the same intended roots. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows this contracted-trie operating curve explicitly.
- Results are comparable only within the same language and benchmark family.
- The historical Porter badge is retired; no JMH badge JSON is generated.

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# Italian Stemmer Benchmarks
This page reports same-language stemming benchmarks for Italian. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `IT_IT` | 10,009 | 337,546 | 20,004 | 317,542 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **337,546**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 302,171 | 89.520% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 12,348 | 3.658% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 20,013 | 5.929% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 3,014 | 0.893% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.056% | 98.997% | 100.000% | Full Radixor dictionary patch-command stemmer. |
| Lucene ItalianLightStemFilter | 0.466% | 0.479% | 0.270% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Lucene SnowballFilter | 0.041% | 0.043% | 0.010% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 0.041% | 0.043% | 0.010% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `italianRadixor` | 24.491 | 3.128 | 77.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene ItalianLightStemFilter | `italianLuceneItalianLightStemFilter` | 15.977 | 1.041 | 50.3 | 0.652 | Light Italian suffix stemmer. |
| Official Snowball direct | `snowballDirect[ITALIAN]` | 109.526 | 12.572 | 344.9 | 4.472 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[ITALIAN]` | 116.260 | 7.459 | 366.1 | 4.747 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `IT_IT` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/it_it/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.996507** among 4 deterministic stemmers. The runner-up is `SNOWBALL ITALIAN DIRECT` at 0.866189, a difference of 0.130318. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.996512** among 4 deterministic stemmers. The runner-up is `SNOWBALL ITALIAN DIRECT` at 0.866205, a difference of 0.130307. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996507|124172 / 53638521211 (0.000231%)|42908 / 6143814 (0.698394%)|0.982618|0.986492|0.986512|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.866189|504775 / 53638521211 (0.000941%)|1644164 / 6143814 (26.761292%)|0.859975|0.807240|0.811470|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.866189|504775 / 53638521211 (0.000941%)|1644164 / 6143814 (26.761292%)|0.859975|0.807240|0.811470|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.508926|10589 / 53638521211 (0.000020%)|6034130 / 6143814 (98.214725%)|0.082782|0.035020|0.127588|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.980053|0.993016|0.999998|0.996507|0.999997|0.000003|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.899134|0.732387|0.999991|0.866189|0.999960|0.000040|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.899134|0.732387|0.999991|0.866189|0.999960|0.000040|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.911959|0.017853|1.000000|0.508926|0.999887|0.000113|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.982618|0.986492|0.990396|0.973344|0.986513|0.986512|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.859975|0.807240|0.760598|0.676783|0.811489|0.811470|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.859975|0.807240|0.760598|0.676783|0.811489|0.811470|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.082782|0.035020|0.022207|0.017822|0.127597|0.127588|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986490|0.995780|0.997113|0.996446|0.996446|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.807220|0.987994|0.933408|0.959925|0.959925|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.807220|0.987994|0.933408|0.959925|0.959925|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.035016|0.997481|0.737537|0.848037|0.848037|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|6100906|124172|42908|53638397039|124172 / 53638521211|42908 / 6143814|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|4499650|504775|1644164|53638016436|504775 / 53638521211|1644164 / 6143814|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|4499650|504775|1644164|53638016436|504775 / 53638521211|1644164 / 6143814|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|109684|10589|6034130|53638510622|10589 / 53638521211|6034130 / 6143814|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999993|0 / 53638521211 (0.000000%)|80 / 6143814 (0.001302%)|0.999997|0.999993|0.999993|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0.999987|1.000000|0.999993|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999997|0.999993|0.999990|0.999987|0.999993|0.999993|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|6143734|0|80|53638521211|0 / 53638521211|80 / 6143814|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999992|170950 / 53638521211 (0.000319%)|80 / 6143814 (0.001302%)|0.978222|0.986272|0.986363|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.972928|0.999987|0.999997|0.999992|0.999997|0.000003|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.978222|0.986272|0.994455|0.972916|0.986365|0.986363|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|6143734|170950|80|53638350261|170950 / 53638521211|80 / 6143814|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|42828|124172|46778|6254|1.909321%|4|334175|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996512|124171 / 53611667072 (0.000232%)|42828 / 6142174 (0.697278%)|0.982617|0.986495|0.986515|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.866205|504774 / 53611667072 (0.000942%)|1643522 / 6142174 (26.757985%)|0.859970|0.807252|0.811479|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.866205|504774 / 53611667072 (0.000942%)|1643522 / 6142174 (26.757985%)|0.859970|0.807252|0.811479|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.508927|10588 / 53611667072 (0.000020%)|6032516 / 6142174 (98.214671%)|0.082784|0.035021|0.127589|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.980048|0.993027|0.999998|0.996512|0.999997|0.000003|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.899114|0.732420|0.999991|0.866205|0.999960|0.000040|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.899114|0.732420|0.999991|0.866205|0.999960|0.000040|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.911947|0.017853|1.000000|0.508927|0.999887|0.000113|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.982617|0.986495|0.990404|0.973350|0.986516|0.986515|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.859970|0.807252|0.760624|0.676800|0.811498|0.811479|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.859970|0.807252|0.760624|0.676800|0.811498|0.811479|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.082784|0.035021|0.022208|0.017823|0.127598|0.127589|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986493|0.995780|0.997115|0.996447|0.996447|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|0.807232|0.987991|0.933413|0.959927|0.959927|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|0.807232|0.987991|0.933413|0.959927|0.959927|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.035017|0.997481|0.737534|0.848035|0.848035|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|6099346|124171|42828|53611542901|124171 / 53611667072|42828 / 6142174|
|2|SNOWBALL ITALIAN DIRECT|PRIMARY_OUTPUT|4498652|504774|1643522|53611162298|504774 / 53611667072|1643522 / 6142174|
|3|SNOWBALL ITALIAN LUCENE FILTER|PRIMARY_OUTPUT|4498652|504774|1643522|53611162298|504774 / 53611667072|1643522 / 6142174|
|4|ITALIAN LUCENE ITALIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|109658|10588|6032516|53611656484|10588 / 53611667072|6032516 / 6142174|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 53611667072 (0.000000%)|0 / 6142174 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|6142174|0|0|53611667072|0 / 53611667072|0 / 6142174|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|170949 / 53611667072 (0.000319%)|0 / 6142174 (0.000000%)|0.978219|0.986275|0.986366|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.972922|1.000000|0.999997|0.999998|0.999997|0.000003|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.978219|0.986275|0.994464|0.972922|0.986368|0.986366|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|6142174|170949|0|53611496123|170949 / 53611667072|0 / 6142174|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|42828|124171|46778|6252|1.909188%|4|334089|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `IT_IT`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Norwegian Bokmal Stemmer Benchmarks
This page reports same-language stemming benchmarks for Norwegian Bokmal. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `NB_NO` | 17,929 | 90,757 | 33,376 | 57,381 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **90,757**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 1,500 | 1.653% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 4,296 | 4.734% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 47,619 | 52.469% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 34,420 | 37.925% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 2,922 | 3.220% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 96.852% | 97.637% | 95.503% | Full Radixor dictionary patch-command stemmer. |
| Lucene NorwegianMinimalStemFilter | 57.107% | 53.913% | 62.599% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Official Snowball direct | 54.824% | 51.791% | 60.040% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene SnowballFilter | 54.803% | 51.780% | 60.001% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Lucene NorwegianLightStemFilter | 52.136% | 50.616% | 54.749% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `norwegianBokmalRadixor` | 3.631 | 1.377 | 63.3 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene NorwegianMinimalStemFilter | `norwegianBokmalLuceneNorwegianMinimalStemFilter` | 2.910 | 0.177 | 50.7 | 0.801 | Minimal Norwegian suffix reducer. |
| Lucene NorwegianLightStemFilter | `norwegianBokmalLuceneNorwegianLightStemFilter` | 3.335 | 0.116 | 58.1 | 0.919 | Light Norwegian suffix stemmer. |
| Official Snowball direct | `snowballDirect[NORWEGIAN_BOKMAL]` | 4.277 | 0.082 | 74.5 | 1.178 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_BOKMAL]` | 6.077 | 0.208 | 105.9 | 1.674 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NB_NO` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/nb_no/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974783** among 5 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN BOKMAL DIRECT` at 0.874964, a difference of 0.099819. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.975000** among 5 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN BOKMAL DIRECT` at 0.874991, a difference of 0.100009. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.974783|11482 / 2835618215 (0.000405%)|7170 / 142180 (5.042903%)|0.927078|0.935387|0.935488|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.874964|23997 / 2835618215 (0.000846%)|35554 / 142180 (25.006330%)|0.802095|0.781707|0.782399|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.874834|24046 / 2835618215 (0.000848%)|35591 / 142180 (25.032353%)|0.801759|0.781401|0.782091|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.850006|25171 / 2835618215 (0.000888%)|42651 / 142180 (29.997890%)|0.776381|0.745871|0.747464|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.832414|14772 / 2835618215 (0.000521%)|47654 / 142180 (33.516669%)|0.815763|0.751764|0.758263|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.921620|0.949571|0.999996|0.974783|0.999993|0.000007|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.816288|0.749937|0.999992|0.874964|0.999979|0.000021|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.815930|0.749676|0.999992|0.874834|0.999979|0.000021|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.798148|0.700021|0.999991|0.850006|0.999976|0.000024|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.864847|0.664833|0.999995|0.832414|0.999978|0.000022|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.927078|0.935387|0.943846|0.878617|0.935491|0.935488|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.802095|0.781707|0.762330|0.641641|0.782409|0.782399|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.801759|0.781401|0.762052|0.641229|0.782102|0.782091|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.776381|0.745871|0.717668|0.594732|0.747476|0.747464|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.815763|0.751764|0.697076|0.602261|0.758274|0.758263|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.935384|0.993354|0.994615|0.993984|0.993984|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.781696|0.988328|0.971120|0.979648|0.979648|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.781391|0.988295|0.971086|0.979615|0.979615|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.745859|0.987774|0.965622|0.976573|0.976573|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.751753|0.992089|0.962516|0.977079|0.977079|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|135010|11482|7170|2835606733|11482 / 2835618215|7170 / 142180|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|106626|23997|35554|2835594218|23997 / 2835618215|35554 / 142180|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|106589|24046|35591|2835594169|24046 / 2835618215|35591 / 142180|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|99529|25171|42651|2835593044|25171 / 2835618215|42651 / 142180|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|94526|14772|47654|2835603443|14772 / 2835618215|47654 / 142180|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 2835618215 (0.000000%)|0 / 142180 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|142180|0|0|2835618215|0 / 2835618215|0 / 142180|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999996|20161 / 2835618215 (0.000711%)|0 / 142180 (0.000000%)|0.898118|0.933794|0.935844|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.875811|1.000000|0.999993|0.999996|0.999993|0.000007|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.898118|0.933794|0.972422|0.875811|0.935848|0.935844|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|142180|20161|0|2835598054|20161 / 2835618215|0 / 142180|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|7170|11482|8679|4237|5.626079%|9|79825|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.975000|11482 / 2831176784 (0.000406%)|7104 / 142091 (4.999613%)|0.927151|0.935591|0.935695|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.874991|23997 / 2831176784 (0.000848%)|35524 / 142091 (25.000880%)|0.802043|0.781698|0.782388|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.874798|23993 / 2831176784 (0.000847%)|35579 / 142091 (25.039587%)|0.801914|0.781464|0.782161|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.849947|25118 / 2831176784 (0.000887%)|42641 / 142091 (30.009642%)|0.776513|0.745896|0.747500|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.832344|14719 / 2831176784 (0.000520%)|47644 / 142091 (33.530625%)|0.815950|0.751796|0.758325|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.921608|0.950004|0.999996|0.975000|0.999993|0.000007|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.816205|0.749991|0.999992|0.874991|0.999979|0.000021|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.816153|0.749604|0.999992|0.874798|0.999979|0.000021|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.798359|0.699904|0.999991|0.849947|0.999976|0.000024|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.865169|0.664694|0.999995|0.832344|0.999978|0.000022|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.927151|0.935591|0.944186|0.878976|0.935698|0.935695|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.802043|0.781698|0.762360|0.641630|0.782398|0.782388|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.801914|0.781464|0.762031|0.641314|0.782171|0.782161|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.776513|0.745896|0.717603|0.594765|0.747512|0.747500|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.815950|0.751796|0.696995|0.602302|0.758335|0.758325|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.935587|0.993348|0.994694|0.994020|0.994020|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|0.781688|0.988318|0.971127|0.979647|0.979647|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|0.781454|0.988310|0.971074|0.979616|0.979616|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.745885|0.987789|0.965603|0.976570|0.976570|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.751785|0.992107|0.962494|0.977076|0.977076|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|134987|11482|7104|2831165302|11482 / 2831176784|7104 / 142091|
|2|SNOWBALL NORWEGIAN BOKMAL DIRECT|PRIMARY_OUTPUT|106567|23997|35524|2831152787|23997 / 2831176784|35524 / 142091|
|3|SNOWBALL NORWEGIAN BOKMAL LUCENE FILTER|PRIMARY_OUTPUT|106512|23993|35579|2831152791|23993 / 2831176784|35579 / 142091|
|4|NORWEGIAN BOKMAL LUCENE NORWEGIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|99450|25118|42641|2831151666|25118 / 2831176784|42641 / 142091|
|5|NORWEGIAN BOKMAL LUCENE NORWEGIAN MINIMAL STEM FILTER|PRIMARY_OUTPUT|94447|14719|47644|2831162065|14719 / 2831176784|47644 / 142091|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 2831176784 (0.000000%)|0 / 142091 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|142091|0|0|2831176784|0 / 2831176784|0 / 142091|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999996|20161 / 2831176784 (0.000712%)|0 / 142091 (0.000000%)|0.898061|0.933756|0.935808|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.875743|1.000000|0.999993|0.999996|0.999993|0.000007|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.898061|0.933756|0.972405|0.875743|0.935811|0.935808|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|142091|20161|0|2831156623|20161 / 2831176784|0 / 142091|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|7104|11482|8679|4204|5.586637%|9|79733|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `NB_NO`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Norwegian Nynorsk Stemmer Benchmarks
This page reports same-language stemming benchmarks for Norwegian Nynorsk. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `NN_NO` | 4,688 | 19,651 | 6,089 | 13,562 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **19,651**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 224 | 1.140% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 1,505 | 7.659% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 11,017 | 56.063% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 6,427 | 32.706% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 478 | 2.432% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 93.089% | 91.395% | 96.863% | Full Radixor dictionary patch-command stemmer. |
| Official Snowball direct | 60.974% | 60.212% | 62.670% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene SnowballFilter | 60.918% | 60.146% | 62.638% | Lucene TokenFilter integration path around the Snowball algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `radixor[NORWEGIAN_NYNORSK]` | 0.571 | 0.012 | 42.1 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Official Snowball direct | `snowballDirect[NORWEGIAN_NYNORSK]` | 0.919 | 0.038 | 67.7 | 1.609 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[NORWEGIAN_NYNORSK]` | 1.309 | 0.015 | 96.5 | 2.292 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `NN_NO` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/nn_no/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.935777** among 3 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN NYNORSK DIRECT` at 0.858908, a difference of 0.076869. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.935853** among 3 deterministic stemmers. The runner-up is `SNOWBALL NORWEGIAN NYNORSK DIRECT` at 0.859037, a difference of 0.076816. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.935777|6230 / 166491473 (0.003742%)|3936 / 30652 (12.840924%)|0.822355|0.840152|0.840669|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.858908|8274 / 166491473 (0.004970%)|8648 / 30652 (28.213493%)|0.724941|0.722271|0.722234|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.858484|8295 / 166491473 (0.004982%)|8674 / 30652 (28.298317%)|0.724180|0.721477|0.721440|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.810903|0.871591|0.999963|0.935777|0.999939|0.000061|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.726732|0.717865|0.999950|0.858908|0.999898|0.000102|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.725993|0.717017|0.999950|0.858484|0.999898|0.000102|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.822355|0.840152|0.858737|0.724364|0.840699|0.840669|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.724941|0.722271|0.719621|0.565278|0.722285|0.722234|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.724180|0.721477|0.718794|0.564305|0.721491|0.721440|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.840122|0.983845|0.986802|0.985321|0.985321|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.722221|0.980542|0.964998|0.972708|0.972708|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.721426|0.980461|0.964862|0.972599|0.972599|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|26716|6230|3936|166485243|6230 / 166491473|3936 / 30652|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|22004|8274|8648|166483199|8274 / 166491473|8648 / 30652|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|21978|8295|8674|166483178|8295 / 166491473|8674 / 30652|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 166491473 (0.000000%)|0 / 30652 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|30652|0|0|166491473|0 / 166491473|0 / 30652|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999960|13214 / 166491473 (0.007937%)|0 / 30652 (0.000000%)|0.743562|0.822674|0.835888|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.698764|1.000000|0.999921|0.999960|0.999921|0.000079|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.743562|0.822674|0.920624|0.698764|0.835921|0.835888|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|30652|13214|0|166478259|13214 / 166491473|0 / 30652|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|3936|6230|6984|2404|13.172603%|5|21513|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.935853|6230 / 165926276 (0.003755%)|3924 / 30595 (12.825625%)|0.822169|0.840084|0.840609|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.859037|8274 / 165926276 (0.004987%)|8624 / 30595 (28.187612%)|0.724757|0.722255|0.722216|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.858661|8274 / 165926276 (0.004987%)|8647 / 30595 (28.262788%)|0.724438|0.721772|0.721734|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.810644|0.871744|0.999962|0.935853|0.999939|0.000061|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.726434|0.718124|0.999950|0.859037|0.999898|0.000102|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.726226|0.717372|0.999950|0.858661|0.999898|0.000102|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.822169|0.840084|0.858798|0.724263|0.840639|0.840609|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.724757|0.722255|0.719771|0.565258|0.722267|0.722216|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.724438|0.721772|0.719126|0.564666|0.721785|0.721734|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.840054|0.983815|0.986842|0.985326|0.985326|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|0.722204|0.980506|0.965065|0.972724|0.972724|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|0.721721|0.980506|0.964945|0.972663|0.972663|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|26671|6230|3924|165920046|6230 / 165926276|3924 / 30595|
|2|SNOWBALL NORWEGIAN NYNORSK DIRECT|PRIMARY_OUTPUT|21971|8274|8624|165918002|8274 / 165926276|8624 / 30595|
|3|SNOWBALL NORWEGIAN NYNORSK LUCENE FILTER|PRIMARY_OUTPUT|21948|8274|8647|165918002|8274 / 165926276|8647 / 30595|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 165926276 (0.000000%)|0 / 30595 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|30595|0|0|165926276|0 / 165926276|0 / 30595|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999960|13214 / 165926276 (0.007964%)|0 / 30595 (0.000000%)|0.743207|0.822402|0.835654|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.698372|1.000000|0.999920|0.999960|0.999920|0.000080|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.743207|0.822402|0.920488|0.698372|0.835687|0.835654|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|30595|13214|0|165913062|13214 / 165926276|0 / 30595|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|3924|6230|6984|2399|13.167572%|5|21477|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `NN_NO`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Persian Stemmer Benchmarks
This page reports same-language stemming benchmarks for Persian. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `FA_IR` | 69 | 3,770 | 138 | 3,632 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **3,770**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `DeletePrefixCommand` | Deletes one or more leading characters from the word form in forward traversal. | 65 | 1.724% |
| `ForwardCompoundCommand` | Applies a multi-step forward patch made from skip, delete, insert, and replace operations. | 3,567 | 94.615% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 138 | 3.660% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 95.836% | 95.677% | 100.000% | Full Radixor dictionary patch-command stemmer. |
| Lucene PersianStemFilter | 1.485% | 0.000% | 40.580% | Lucene Persian suffix stemmer with required normalization in the measured path. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `persianRadixor` | 0.245 | 0.025 | 49.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene PersianStemFilter | `persianLucenePersianStemFilter` | 0.466 | 0.015 | 93.3 | 1.902 | Persian suffix stemmer with Lucene normalization in the measured path. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `FA_IR` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/fa_ir/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974922** among 2 deterministic stemmers. The runner-up is `PERSIAN LUCENE PERSIAN STEM FILTER` at 0.502171, a difference of 0.472751. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.974922** among 2 deterministic stemmers. The runner-up is `PERSIAN LUCENE PERSIAN STEM FILTER` at 0.502171, a difference of 0.472751. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **4 result rows**, **2 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.974922|8621 / 6748402 (0.127749%)|4812 / 98448 (4.887860%)|0.922566|0.933071|0.932249|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.502171|179 / 6748402 (0.002652%)|98018 / 98448 (99.563221%)|0.021312|0.008682|0.054801|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.915693|0.951121|0.998723|0.974922|0.998038|0.001962|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.706076|0.004368|0.999973|0.502171|0.985658|0.014342|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.922566|0.933071|0.943818|0.874539|0.933239|0.932249|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.021312|0.008682|0.005451|0.004360|0.055534|0.054801|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.932076|0.980343|0.984586|0.982460|0.982460|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.008507|0.985686|0.520347|0.681125|0.681125|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|93636|8621|4812|6739781|8621 / 6748402|4812 / 98448|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|430|179|98018|6748223|179 / 6748402|98018 / 98448|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6748402 (0.000000%)|0 / 98448 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|98448|0|0|6748402|0 / 6748402|0 / 98448|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999005|13433 / 6748402 (0.199055%)|0 / 98448 (0.000000%)|0.901585|0.936133|0.937114|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.879935|1.000000|0.998009|0.999005|0.998038|0.001962|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.901585|0.936133|0.973435|0.879935|0.938048|0.937114|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|98448|13433|0|6734969|13433 / 6748402|0 / 98448|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|4812|8621|4812|314|8.484193%|2|4015|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **4 result rows**, **2 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.974922|8621 / 6748402 (0.127749%)|4812 / 98448 (4.887860%)|0.922566|0.933071|0.932249|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.502171|179 / 6748402 (0.002652%)|98018 / 98448 (99.563221%)|0.021312|0.008682|0.054801|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.915693|0.951121|0.998723|0.974922|0.998038|0.001962|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.706076|0.004368|0.999973|0.502171|0.985658|0.014342|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.922566|0.933071|0.943818|0.874539|0.933239|0.932249|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.021312|0.008682|0.005451|0.004360|0.055534|0.054801|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.932076|0.980343|0.984586|0.982460|0.982460|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|0.008507|0.985686|0.520347|0.681125|0.681125|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|93636|8621|4812|6739781|8621 / 6748402|4812 / 98448|
|2|PERSIAN LUCENE PERSIAN STEM FILTER|PRIMARY_OUTPUT|430|179|98018|6748223|179 / 6748402|98018 / 98448|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6748402 (0.000000%)|0 / 98448 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|98448|0|0|6748402|0 / 6748402|0 / 98448|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999005|13433 / 6748402 (0.199055%)|0 / 98448 (0.000000%)|0.901585|0.936133|0.937114|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.879935|1.000000|0.998009|0.999005|0.998038|0.001962|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.901585|0.936133|0.973435|0.879935|0.938048|0.937114|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|98448|13433|0|6734969|13433 / 6748402|0 / 98448|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|4812|8621|4812|314|8.484193%|2|4015|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `FA_IR`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Polish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Polish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `PL_PL` | 9,990 | 132,308 | 19,957 | 112,351 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **132,308**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 1,719 | 1.299% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 53,303 | 40.287% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 37,051 | 28.004% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 20,415 | 15.430% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 19,820 | 14.980% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 98.837% | 98.744% | 99.359% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 89.545% | 88.272% | 96.713% | Benchmark-only Polish Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene MorfologikFilter | 87.729% | 86.606% | 94.047% | Dictionary-based path; Morfologik can emit multiple terms. |
| Lucene StempelFilter | 70.009% | 69.262% | 74.220% | Lucene TokenFilter integration path for table-driven Polish Stempel. |
| Lucene StempelStemmer direct | 70.009% | 69.262% | 74.220% | Direct table-driven Polish Stempel stemmer API. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `polishRadixor` | 9.049 | 0.485 | 80.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 483.316 | 11.455 | 4301.8 | 53.408 | Benchmark-only Polish Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene StempelStemmer direct | `polishLuceneStempelStemmerDirect` | 41.932 | 1.916 | 373.2 | 4.634 | Direct table-driven Polish Stempel stemmer API. |
| Lucene StempelFilter | `polishLuceneStempelFilter` | 45.277 | 13.693 | 403.0 | 5.003 | Lucene TokenFilter integration path for table-driven Polish Stempel. |
| Lucene MorfologikFilter | `polishLuceneMorfologikFilter` | 135.763 | 31.634 | 1208.4 | 15.002 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `PL_PL` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/pl_pl/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.990388** among 5 deterministic stemmers. The runner-up is `POLISH LUCENE MORFOLOGIK FILTER` at 0.948154, a difference of 0.042234. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.990579** among 5 deterministic stemmers. The runner-up is `POLISH LUCENE MORFOLOGIK FILTER` at 0.948177, a difference of 0.042402. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **11 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990388|13669 / 7482478003 (0.000183%)|21547 / 1120967 (1.922180%)|0.986324|0.984237|0.984241|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.948154|99228 / 7482478003 (0.001326%)|116220 / 1120967 (10.367834%)|0.907324|0.903167|0.903179|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.933222|52652 / 7482478003 (0.000704%)|149705 / 1120967 (13.354987%)|0.930930|0.905656|0.906571|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.855748|66669 / 7482478003 (0.000891%)|323394 / 1120967 (28.849556%)|0.871106|0.803515|0.810296|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.855748|66669 / 7482478003 (0.000891%)|323394 / 1120967 (28.849556%)|0.871106|0.803515|0.810296|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987720|0.980778|0.999998|0.990388|0.999995|0.000005|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.910118|0.896322|0.999987|0.948154|0.999971|0.000029|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.948578|0.866450|0.999993|0.933222|0.999973|0.000027|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.922858|0.711504|0.999991|0.855748|0.999948|0.000052|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.922858|0.711504|0.999991|0.855748|0.999948|0.000052|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986324|0.984237|0.982159|0.968963|0.984243|0.984241|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.907324|0.903167|0.899047|0.823432|0.903193|0.903179|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.930930|0.905656|0.881718|0.827579|0.906584|0.906571|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.871106|0.803515|0.745659|0.671564|0.810320|0.810296|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.871106|0.803515|0.745659|0.671564|0.810320|0.810296|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.984234|0.996967|0.996469|0.996718|0.996718|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.903153|0.990022|0.977054|0.983495|0.983495|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.905642|0.994546|0.970520|0.982386|0.982386|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.803490|0.991767|0.931069|0.960460|0.960460|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.803490|0.991767|0.931069|0.960460|0.960460|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|1099420|13669|21547|7482464334|13669 / 7482478003|21547 / 1120967|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|1004747|99228|116220|7482378775|99228 / 7482478003|116220 / 1120967|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|971262|52652|149705|7482425351|52652 / 7482478003|149705 / 1120967|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|797573|66669|323394|7482411334|66669 / 7482478003|323394 / 1120967|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|797573|66669|323394|7482411334|66669 / 7482478003|323394 / 1120967|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 7482478003 (0.000000%)|0 / 1120967 (0.000000%)|1.000000|1.000000|1.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.987570|85532 / 7482478003 (0.001143%)|27855 / 1120967 (2.484908%)|0.936598|0.950693|0.950985|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.963982|42213 / 7482478003 (0.000564%)|80743 / 1120967 (7.202977%)|0.954209|0.944197|0.944333|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.927432|0.975151|0.999989|0.987570|0.999985|0.000015|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.961002|0.927970|0.999994|0.963982|0.999984|0.000016|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.936598|0.950693|0.965218|0.906020|0.950992|0.950985|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.954209|0.944197|0.934394|0.894293|0.944342|0.944333|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1120967|0|0|7482478003|0 / 7482478003|0 / 1120967|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|1093112|85532|27855|7482392471|85532 / 7482478003|27855 / 1120967|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|1040224|42213|80743|7482435790|42213 / 7482478003|80743 / 1120967|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999997|38073 / 7482478003 (0.000509%)|0 / 1120967 (0.000000%)|0.973547|0.983301|0.983436|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.987566|143096 / 7482478003 (0.001912%)|27855 / 1120967 (2.484908%)|0.901045|0.927476|0.928576|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.963980|82745 / 7482478003 (0.001106%)|80743 / 1120967 (7.202977%)|0.926646|0.927142|0.927132|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.967151|1.000000|0.999995|0.999997|0.999995|0.000005|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.884246|0.975151|0.999981|0.987566|0.999977|0.000023|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926316|0.927970|0.999989|0.963980|0.999978|0.000022|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.973547|0.983301|0.993253|0.967151|0.983438|0.983436|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.901045|0.927476|0.955505|0.864761|0.928587|0.928576|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926646|0.927142|0.927639|0.864180|0.927143|0.927132|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|1120967|38073|0|7482439930|38073 / 7482478003|0 / 1120967|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|1093112|143096|27855|7482334907|143096 / 7482478003|27855 / 1120967|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|1040224|82745|80743|7482395258|82745 / 7482478003|80743 / 1120967|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL POLISH LUCENE FILTER|68962|10439|30093|11447|9.356634%|6|135231|
|POLISH LUCENE MORFOLOGIK FILTER|88365|13696|43868|12873|10.522229%|5|136636|
|Radixor|21547|13669|24404|2866|2.342632%|4|125778|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **11 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990579|13669 / 7310252699 (0.000187%)|21000 / 1114651 (1.883998%)|0.986350|0.984397|0.984400|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.948177|99224 / 7310252699 (0.001357%)|115513 / 1114651 (10.363154%)|0.906972|0.902966|0.902976|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.933309|51950 / 7310252699 (0.000711%)|148667 / 1114651 (13.337538%)|0.931269|0.905928|0.906847|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.856382|66274 / 7310252699 (0.000907%)|320158 / 1114651 (28.722712%)|0.871591|0.804380|0.811082|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.856382|66274 / 7310252699 (0.000907%)|320158 / 1114651 (28.722712%)|0.871591|0.804380|0.811082|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987656|0.981160|0.999998|0.990579|0.999995|0.000005|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.909662|0.896368|0.999986|0.948177|0.999971|0.000029|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.948965|0.866625|0.999993|0.933309|0.999973|0.000027|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.923006|0.712773|0.999991|0.856382|0.999947|0.000053|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.923006|0.712773|0.999991|0.856382|0.999947|0.000053|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986350|0.984397|0.982452|0.969274|0.984403|0.984400|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.906972|0.902966|0.898996|0.823098|0.902991|0.902976|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.931269|0.905928|0.881929|0.828033|0.906861|0.906847|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.871591|0.804380|0.746792|0.672772|0.811106|0.811082|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.871591|0.804380|0.746792|0.672772|0.811106|0.811082|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.984395|0.996926|0.996647|0.996786|0.996786|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.902952|0.989889|0.977012|0.983408|0.983408|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|0.905914|0.994584|0.970514|0.982402|0.982402|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|0.804354|0.991711|0.931318|0.960566|0.960566|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|0.804354|0.991711|0.931318|0.960566|0.960566|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|1093651|13669|21000|7310239030|13669 / 7310252699|21000 / 1114651|
|2|POLISH LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|999138|99224|115513|7310153475|99224 / 7310252699|115513 / 1114651|
|3|HUNSPELL POLISH LUCENE FILTER|PRIMARY_OUTPUT|965984|51950|148667|7310200749|51950 / 7310252699|148667 / 1114651|
|4|POLISH LUCENE STEMPEL DIRECT|PRIMARY_OUTPUT|794493|66274|320158|7310186425|66274 / 7310252699|320158 / 1114651|
|5|POLISH LUCENE STEMPEL FILTER|PRIMARY_OUTPUT|794493|66274|320158|7310186425|66274 / 7310252699|320158 / 1114651|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 7310252699 (0.000000%)|0 / 1114651 (0.000000%)|1.000000|1.000000|1.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.987661|85532 / 7310252699 (0.001170%)|27494 / 1114651 (2.466602%)|0.936331|0.950586|0.950885|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.963946|41671 / 7310252699 (0.000570%)|80368 / 1114651 (7.210149%)|0.954406|0.944290|0.944429|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.927063|0.975334|0.999988|0.987661|0.999985|0.000015|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.961271|0.927899|0.999994|0.963946|0.999983|0.000017|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.936331|0.950586|0.965282|0.905826|0.950892|0.950885|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|0.954406|0.944290|0.934386|0.894459|0.944437|0.944429|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1114651|0|0|7310252699|0 / 7310252699|0 / 1114651|
|2|POLISH LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|1087157|85532|27494|7310167167|85532 / 7310252699|27494 / 1114651|
|3|HUNSPELL POLISH LUCENE FILTER|ANY_CANDIDATE|1034283|41671|80368|7310211028|41671 / 7310252699|80368 / 1114651|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999997|38073 / 7310252699 (0.000521%)|0 / 1114651 (0.000000%)|0.973401|0.983208|0.983344|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.987657|143085 / 7310252699 (0.001957%)|27494 / 1114651 (2.466602%)|0.900618|0.927255|0.928372|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.963944|81865 / 7310252699 (0.001120%)|80368 / 1114651 (7.210149%)|0.926903|0.927276|0.927265|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.966971|1.000000|0.999995|0.999997|0.999995|0.000005|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.883694|0.975334|0.999980|0.987657|0.999977|0.000023|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926654|0.927899|0.999989|0.963944|0.999978|0.000022|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.973401|0.983208|0.993215|0.966971|0.983347|0.983344|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.900618|0.927255|0.955516|0.864376|0.928384|0.928372|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|0.926903|0.927276|0.927649|0.864412|0.927276|0.927265|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|1114651|38073|0|7310214626|38073 / 7310252699|0 / 1114651|
|2|POLISH LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|1087157|143085|27494|7310109614|143085 / 7310252699|27494 / 1114651|
|3|HUNSPELL POLISH LUCENE FILTER|ALL_CANDIDATES|1034283|81865|80368|7310170834|81865 / 7310252699|80368 / 1114651|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL POLISH LUCENE FILTER|68299|10279|29915|11265|9.315692%|6|133595|
|POLISH LUCENE MORFOLOGIK FILTER|88019|13692|43861|12763|10.554476%|5|135105|
|Radixor|21000|13669|24404|2780|2.298946%|4|124274|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `PL_PL`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Portuguese Stemmer Benchmarks
This page reports same-language stemming benchmarks for Portuguese. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `PT_PT` | 4,001 | 215,490 | 8,002 | 207,488 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **215,490**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 3,806 | 1.766% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 120,691 | 56.008% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 71,284 | 33.080% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 8,003 | 3.714% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 11,706 | 5.432% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.815% | 99.808% | 100.000% | Full Radixor dictionary patch-command stemmer. |
| Lucene PortugueseLightStemFilter | 8.966% | 5.558% | 97.326% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Lucene PortugueseMinimalStemFilter | 5.539% | 1.896% | 100.000% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Lucene SnowballFilter | 0.625% | 0.558% | 2.374% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 0.625% | 0.558% | 2.374% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene PortugueseStemFilter | 0.312% | 0.308% | 0.425% | Portuguese RSLP-style Lucene TokenFilter stemmer. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `portugueseRadixor` | 12.109 | 0.698 | 58.4 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene PortugueseLightStemFilter | `portugueseLucenePortugueseLightStemFilter` | 11.172 | 1.870 | 53.8 | 0.923 | Light Portuguese suffix stemmer. |
| Lucene PortugueseMinimalStemFilter | `portugueseLucenePortugueseMinimalStemFilter` | 16.038 | 1.752 | 77.3 | 1.325 | Minimal Portuguese suffix reducer. |
| Official Snowball direct | `snowballDirect[PORTUGUESE]` | 53.725 | 5.356 | 258.9 | 4.437 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[PORTUGUESE]` | 57.457 | 1.182 | 276.9 | 4.745 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
| Lucene PortugueseStemFilter | `portugueseLucenePortugueseStemFilter` | 165.447 | 40.334 | 797.4 | 13.663 | Portuguese RSLP-style Lucene TokenFilter. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `PT_PT` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/pt_pt/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.998502** among 6 deterministic stemmers. The runner-up is `SNOWBALL PORTUGUESE DIRECT` at 0.938800, a difference of 0.059702. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.998502** among 6 deterministic stemmers. The runner-up is `SNOWBALL PORTUGUESE DIRECT` at 0.938800, a difference of 0.059702. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **8 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.998502|20678 / 22358203756 (0.000092%)|16444 / 5489060 (0.299578%)|0.996389|0.996620|0.996619|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.846459|99075 / 22358203756 (0.000443%)|1685572 / 5489060 (30.707844%)|0.901330|0.809975|0.821750|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.513648|2511 / 22358203756 (0.000011%)|5339230 / 5489060 (97.270389%)|0.122843|0.053118|0.163828|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.503954|598 / 22358203756 (0.000003%)|5445654 / 5489060 (99.209227%)|0.038310|0.015690|0.088308|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996236|0.997004|0.999999|0.998502|0.999998|0.000002|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.974613|0.692922|0.999996|0.846459|0.999920|0.000080|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.983517|0.027296|1.000000|0.513648|0.999761|0.000239|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.986410|0.007908|1.000000|0.503954|0.999756|0.000244|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996389|0.996620|0.996850|0.993262|0.996620|0.996619|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.901330|0.809975|0.735434|0.680636|0.821785|0.821750|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.122843|0.053118|0.033885|0.027284|0.163848|0.163828|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.038310|0.015690|0.009865|0.007907|0.088319|0.088308|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996619|0.999299|0.999347|0.999323|0.999323|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.809936|0.996729|0.918475|0.956003|0.956003|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.053105|0.999226|0.720580|0.837330|0.837330|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.015686|0.999664|0.692383|0.818122|0.818122|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|5472616|20678|16444|22358183078|20678 / 22358203756|16444 / 5489060|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|3803488|99075|1685572|22358104681|99075 / 22358203756|1685572 / 5489060|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|149830|2511|5339230|22358201245|2511 / 22358203756|5339230 / 5489060|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|43406|598|5445654|22358203158|598 / 22358203756|5445654 / 5489060|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 22358203756 (0.000000%)|0 / 5489060 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|5489060|0|0|22358203756|0 / 22358203756|0 / 5489060|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|38310 / 22358203756 (0.000171%)|0 / 5489060 (0.000000%)|0.994448|0.996522|0.996528|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.993069|1.000000|0.999998|0.999999|0.999998|0.000002|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.994448|0.996522|0.998606|0.993069|0.996528|0.996528|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|5489060|38310|0|22358165446|38310 / 22358203756|0 / 5489060|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|16444|20678|17632|790|0.373542%|3|212297|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **8 result rows**, **6 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.998502|20678 / 22358203756 (0.000092%)|16444 / 5489060 (0.299578%)|0.996389|0.996620|0.996619|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.938800|167230 / 22358203756 (0.000748%)|671821 / 5489060 (12.239272%)|0.947271|0.919888|0.920940|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.846459|99075 / 22358203756 (0.000443%)|1685572 / 5489060 (30.707844%)|0.901330|0.809975|0.821750|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.513648|2511 / 22358203756 (0.000011%)|5339230 / 5489060 (97.270389%)|0.122843|0.053118|0.163828|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.503954|598 / 22358203756 (0.000003%)|5445654 / 5489060 (99.209227%)|0.038310|0.015690|0.088308|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996236|0.997004|0.999999|0.998502|0.999998|0.000002|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.966450|0.877607|0.999993|0.938800|0.999962|0.000038|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.974613|0.692922|0.999996|0.846459|0.999920|0.000080|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.983517|0.027296|1.000000|0.513648|0.999761|0.000239|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.986410|0.007908|1.000000|0.503954|0.999756|0.000244|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996389|0.996620|0.996850|0.993262|0.996620|0.996619|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.947271|0.919888|0.894045|0.851661|0.920958|0.920940|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.901330|0.809975|0.735434|0.680636|0.821785|0.821750|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.122843|0.053118|0.033885|0.027284|0.163848|0.163828|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.038310|0.015690|0.009865|0.007907|0.088319|0.088308|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.996619|0.999299|0.999347|0.999323|0.999323|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|0.919870|0.996663|0.967924|0.982083|0.982083|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|0.809936|0.996729|0.918475|0.956003|0.956003|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|0.053105|0.999226|0.720580|0.837330|0.837330|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.015686|0.999664|0.692383|0.818122|0.818122|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|5472616|20678|16444|22358183078|20678 / 22358203756|16444 / 5489060|
|2|SNOWBALL PORTUGUESE DIRECT|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
|3|SNOWBALL PORTUGUESE LUCENE FILTER|PRIMARY_OUTPUT|4817239|167230|671821|22358036526|167230 / 22358203756|671821 / 5489060|
|4|PORTUGUESE LUCENE PORTUGUESE STEM FILTER|PRIMARY_OUTPUT|3803488|99075|1685572|22358104681|99075 / 22358203756|1685572 / 5489060|
|5|PORTUGUESE LUCENE PORTUGUESE LIGHT STEM FILTER|PRIMARY_OUTPUT|149830|2511|5339230|22358201245|2511 / 22358203756|5339230 / 5489060|
|6|PORTUGUESE LUCENE PORTUGUESE MINIMAL STEM FILTER|PRIMARY_OUTPUT|43406|598|5445654|22358203158|598 / 22358203756|5445654 / 5489060|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 22358203756 (0.000000%)|0 / 5489060 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|5489060|0|0|22358203756|0 / 22358203756|0 / 5489060|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|38310 / 22358203756 (0.000171%)|0 / 5489060 (0.000000%)|0.994448|0.996522|0.996528|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.993069|1.000000|0.999998|0.999999|0.999998|0.000002|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.994448|0.996522|0.998606|0.993069|0.996528|0.996528|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|5489060|38310|0|22358165446|38310 / 22358203756|0 / 5489060|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|16444|20678|17632|790|0.373542%|3|212297|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `PT_PT`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Russian Stemmer Benchmarks
This page reports same-language stemming benchmarks for Russian. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `RU_RU` | 37,410 | 806,279 | 74,808 | 731,471 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **806,279**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 9,260 | 1.148% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 584,785 | 72.529% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 82,864 | 10.277% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 75,646 | 9.382% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 53,724 | 6.663% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 98.807% | 98.696% | 99.896% | Full Radixor dictionary patch-command stemmer. |
| Lucene RussianLightStemFilter | 9.658% | 8.452% | 21.447% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Lucene SnowballFilter | 9.162% | 8.162% | 18.936% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 9.162% | 8.162% | 18.936% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `russianRadixor` | 89.671 | 3.886 | 122.6 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene RussianLightStemFilter | `russianLuceneRussianLightStemFilter` | 60.522 | 5.310 | 82.7 | 0.675 | Light Russian suffix stemmer. |
| Official Snowball direct | `snowballDirect[RUSSIAN]` | 106.031 | 9.287 | 145.0 | 1.182 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[RUSSIAN]` | 137.512 | 10.801 | 188.0 | 1.534 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `RU_RU` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/ru_ru/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.989827** among 4 deterministic stemmers. The runner-up is `SNOWBALL RUSSIAN LUCENE FILTER` at 0.834876, a difference of 0.154951. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989852** among 4 deterministic stemmers. The runner-up is `SNOWBALL RUSSIAN DIRECT` at 0.834854, a difference of 0.154998. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989827|155850 / 295576291016 (0.000053%)|266302 / 13089505 (2.034470%)|0.986313|0.983806|0.983814|
|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834876|3785790 / 295576291016 (0.001281%)|4322616 / 13089505 (33.023525%)|0.692485|0.683786|0.683923|
|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.834867|3782908 / 295576291016 (0.001280%)|4322849 / 13089505 (33.025305%)|0.692603|0.683851|0.683989|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617692|321183 / 295576291016 (0.000109%)|10008438 / 13089505 (76.461547%)|0.577011|0.373649|0.461687|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987992|0.979655|0.999999|0.989827|0.999999|0.000001|
|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.698408|0.669765|0.999987|0.834876|0.999973|0.000027|
|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.698563|0.669747|0.999987|0.834867|0.999973|0.000027|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.905597|0.235385|0.999999|0.617692|0.999965|0.000035|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986313|0.983806|0.981311|0.968128|0.983815|0.983814|
|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.692485|0.683786|0.675304|0.519510|0.683936|0.683923|
|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.692603|0.683851|0.675318|0.519585|0.684003|0.683989|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.577011|0.373649|0.276278|0.229747|0.461696|0.461687|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.983805|0.997699|0.997274|0.997487|0.997487|
|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.683773|0.974131|0.953674|0.963794|0.963794|
|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.683838|0.974180|0.953661|0.963811|0.963811|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.373638|0.994311|0.870888|0.928516|0.928516|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|12823203|155850|266302|295576135166|155850 / 295576291016|266302 / 13089505|
|2|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|8766889|3785790|4322616|295572505226|3785790 / 295576291016|4322616 / 13089505|
|3|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|8766656|3782908|4322849|295572508108|3782908 / 295576291016|4322849 / 13089505|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|3081067|321183|10008438|295575969833|321183 / 295576291016|10008438 / 13089505|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 295576291016 (0.000000%)|13 / 13089505 (0.000099%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0.999999|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|0.999999|0.999999|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|13089492|0|13|295576291016|0 / 295576291016|13 / 13089505|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|434710 / 295576291016 (0.000147%)|13 / 13089505 (0.000099%)|0.974119|0.983665|0.983796|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.967857|0.999999|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.974119|0.983665|0.993401|0.967856|0.983797|0.983796|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|13089492|434710|13|295575856306|434710 / 295576291016|13 / 13089505|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|266289|155850|278860|19162|2.492190%|4|788492|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **6 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989852|155850 / 295000681652 (0.000053%)|265613 / 13087126 (2.029575%)|0.986322|0.983830|0.983838|
|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.834854|3782908 / 295000681652 (0.001282%)|4322407 / 13087126 (33.027931%)|0.692561|0.683815|0.683953|
|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.834854|3782908 / 295000681652 (0.001282%)|4322407 / 13087126 (33.027931%)|0.692561|0.683815|0.683953|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.617630|318921 / 295000681652 (0.000108%)|10008238 / 13087126 (76.473918%)|0.577038|0.373540|0.461703|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987991|0.979704|0.999999|0.989852|0.999999|0.000001|
|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.698516|0.669721|0.999987|0.834854|0.999973|0.000027|
|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.698516|0.669721|0.999987|0.834854|0.999973|0.000027|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.906139|0.235261|0.999999|0.617630|0.999965|0.000035|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986322|0.983830|0.981350|0.968175|0.983839|0.983838|
|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.692561|0.683815|0.675288|0.519544|0.683967|0.683953|
|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.692561|0.683815|0.675288|0.519544|0.683967|0.683953|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.577038|0.373540|0.276152|0.229664|0.461713|0.461703|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.983829|0.997697|0.997321|0.997509|0.997509|
|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|0.683802|0.974149|0.953634|0.963782|0.963782|
|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|0.683802|0.974149|0.953634|0.963782|0.963782|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|0.373528|0.994350|0.870767|0.928464|0.928464|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|12821513|155850|265613|295000525802|155850 / 295000681652|265613 / 13087126|
|2|SNOWBALL RUSSIAN DIRECT|PRIMARY_OUTPUT|8764719|3782908|4322407|294996898744|3782908 / 295000681652|4322407 / 13087126|
|3|SNOWBALL RUSSIAN LUCENE FILTER|PRIMARY_OUTPUT|8764719|3782908|4322407|294996898744|3782908 / 295000681652|4322407 / 13087126|
|4|RUSSIAN LUCENE RUSSIAN LIGHT STEM FILTER|PRIMARY_OUTPUT|3078888|318921|10008238|295000362731|318921 / 295000681652|10008238 / 13087126|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 295000681652 (0.000000%)|0 / 13087126 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|13087126|0|0|295000681652|0 / 295000681652|0 / 13087126|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999999|434710 / 295000681652 (0.000147%)|0 / 13087126 (0.000000%)|0.974115|0.983663|0.983794|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.967851|1.000000|0.999999|0.999999|0.999999|0.000001|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.974115|0.983663|0.993401|0.967851|0.983794|0.983794|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|13087126|434710|0|295000246942|434710 / 295000681652|0 / 13087126|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|265613|155850|278860|18991|2.472358%|4|787549|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `RU_RU`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Spanish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Spanish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `ES_ES` | 65,059 | 926,393 | 120,121 | 806,272 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **926,393**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 5,367 | 0.579% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 524,682 | 56.637% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 240,872 | 26.001% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 130,089 | 14.043% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 25,383 | 2.740% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 97.459% | 97.544% | 96.891% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 49.074% | 42.656% | 92.154% | Benchmark-only Spanish Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene SpanishMinimalStemFilter | 17.284% | 5.347% | 97.403% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Lucene SpanishPluralStemFilter | 15.140% | 5.802% | 77.820% | Plural-focused suffix reducer; narrow baseline. |
| Lucene SpanishLightStemFilter | 9.577% | 7.088% | 26.279% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Lucene SnowballFilter | 4.889% | 4.287% | 8.932% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 4.889% | 4.287% | 8.930% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `spanishRadixor` | 78.919 | 7.253 | 97.9 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 2079.041 | 193.548 | 2578.6 | 26.344 | Benchmark-only Spanish Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene SpanishMinimalStemFilter | `spanishLuceneSpanishMinimalStemFilter` | 45.596 | 4.639 | 56.6 | 0.578 | Minimal Spanish suffix reducer; narrow baseline. |
| Lucene SpanishLightStemFilter | `spanishLuceneSpanishLightStemFilter` | 42.003 | 1.683 | 52.1 | 0.532 | Light Spanish suffix stemmer. |
| Lucene SpanishPluralStemFilter | `spanishLuceneSpanishPluralStemFilter` | 93.734 | 6.247 | 116.3 | 1.188 | Plural-oriented Spanish suffix reducer. |
| Official Snowball direct | `snowballDirect[SPANISH]` | 171.995 | 11.035 | 213.3 | 2.179 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[SPANISH]` | 211.138 | 17.940 | 261.9 | 2.675 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `ES_ES` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/es_es/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.989295** among 7 deterministic stemmers. The runner-up is `SNOWBALL SPANISH LUCENE FILTER` at 0.652614, a difference of 0.336680. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.989429** among 7 deterministic stemmers. The runner-up is `SNOWBALL SPANISH DIRECT` at 0.652720, a difference of 0.336709. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **11 result rows**, **7 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989295|288483 / 379567318110 (0.000076%)|898652 / 41973336 (2.141007%)|0.990105|0.985755|0.985780|
|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.652614|2230481 / 379567318110 (0.000588%)|29161643 / 41973336 (69.476591%)|0.627151|0.449411|0.509848|
|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.652614|2228819 / 379567318110 (0.000587%)|29161649 / 41973336 (69.476605%)|0.627192|0.449424|0.509876|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.615102|536192 / 379567318110 (0.000141%)|32310860 / 41973336 (76.979490%)|0.583708|0.370408|0.466992|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.514823|147956 / 379567318110 (0.000039%)|40729019 / 41973336 (97.035458%)|0.130864|0.057387|0.162762|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.503874|58578 / 379567318110 (0.000015%)|41648091 / 41973336 (99.225115%)|0.037377|0.015357|0.081026|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.501768|47859 / 379567318110 (0.000013%)|41824873 / 41973336 (99.646292%)|0.017361|0.007041|0.051714|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.993026|0.978590|0.999999|0.989295|0.999997|0.000003|
|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.851718|0.305234|0.999994|0.652614|0.999917|0.000083|
|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.851812|0.305234|0.999994|0.652614|0.999917|0.000083|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.947425|0.230205|0.999999|0.615102|0.999913|0.000087|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.893731|0.029645|1.000000|0.514823|0.999892|0.000108|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.847383|0.007749|1.000000|0.503874|0.999890|0.000110|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.756222|0.003537|1.000000|0.501768|0.999890|0.000110|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990105|0.985755|0.981443|0.971910|0.985781|0.985780|
|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.627151|0.449411|0.350170|0.289832|0.509876|0.509848|
|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.627192|0.449424|0.350173|0.289843|0.509904|0.509876|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.583708|0.370408|0.271278|0.227301|0.467014|0.466992|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.130864|0.057387|0.036752|0.029541|0.162773|0.162762|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.037377|0.015357|0.009664|0.007738|0.081032|0.081026|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.017361|0.007041|0.004416|0.003533|0.051719|0.051714|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.985753|0.995418|0.993266|0.994341|0.994341|
|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.449379|0.981386|0.852461|0.912391|0.912391|
|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.449392|0.981406|0.852463|0.912401|0.912401|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.370381|0.993314|0.790558|0.880414|0.880414|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.057381|0.993824|0.756690|0.859195|0.859195|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.015355|0.995442|0.723731|0.838115|0.838115|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.007040|0.995635|0.710610|0.829316|0.829316|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|41074684|288483|898652|379567029627|288483 / 379567318110|898652 / 41973336|
|2|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|12811693|2230481|29161643|379565087629|2230481 / 379567318110|29161643 / 41973336|
|3|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|12811687|2228819|29161649|379565089291|2228819 / 379567318110|29161649 / 41973336|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|9662476|536192|32310860|379566781918|536192 / 379567318110|32310860 / 41973336|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|1244317|147956|40729019|379567170154|147956 / 379567318110|40729019 / 41973336|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|325245|58578|41648091|379567259532|58578 / 379567318110|41648091 / 41973336|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|148463|47859|41824873|379567270251|47859 / 379567318110|41824873 / 41973336|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999993|2 / 379567318110 (0.000000%)|626 / 41973336 (0.001491%)|0.999997|0.999993|0.999993|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.620065|416345 / 379567318110 (0.000110%)|31894218 / 41973336 (75.986855%)|0.600268|0.384195|0.480192|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0.999985|1.000000|0.999993|1.000000|0.000000|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.960331|0.240131|0.999999|0.620065|0.999915|0.000085|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|0.999997|0.999993|0.999988|0.999985|0.999993|0.999993|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.600268|0.384195|0.282504|0.237773|0.480214|0.480192|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|41972710|2|626|379567318108|2 / 379567318110|626 / 41973336|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|10079118|416345|31894218|379566901765|416345 / 379567318110|31894218 / 41973336|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999991|1349800 / 379567318110 (0.000356%)|626 / 41973336 (0.001491%)|0.974915|0.984168|0.984289|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.620065|888077 / 379567318110 (0.000234%)|31894218 / 41973336 (75.986855%)|0.587073|0.380771|0.469749|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.968843|0.999985|0.999996|0.999991|0.999996|0.000004|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.919024|0.240131|0.999998|0.620065|0.999914|0.000086|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.974915|0.984168|0.993598|0.968829|0.984291|0.984289|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.587073|0.380771|0.281759|0.235156|0.469773|0.469749|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|41972710|1349800|626|379565968310|1349800 / 379567318110|626 / 41973336|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|10079118|888077|31894218|379566430033|888077 / 379567318110|31894218 / 41973336|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL SPANISH LUCENE FILTER|416642|119847|351885|17877|2.051686%|5|890999|
|Radixor|898026|288481|1061317|42637|4.893313%|21|916797|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **11 result rows**, **7 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.989429|276044 / 377860669765 (0.000073%)|885033 / 41863370 (2.114099%)|0.990385|0.986031|0.986056|
|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.652720|2201196 / 377860669765 (0.000583%)|29076352 / 41863370 (69.455354%)|0.627946|0.449839|0.510450|
|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.652720|2201196 / 377860669765 (0.000583%)|29076352 / 41863370 (69.455354%)|0.627946|0.449839|0.510450|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.614999|531181 / 377860669765 (0.000141%)|32234855 / 41863370 (77.000144%)|0.583531|0.370163|0.466854|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.514832|146613 / 377860669765 (0.000039%)|40621522 / 41863370 (97.033569%)|0.130949|0.057424|0.162875|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.503877|57716 / 377860669765 (0.000015%)|41538714 / 41863370 (99.224487%)|0.037409|0.015370|0.081139|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.501770|47148 / 377860669765 (0.000012%)|41715144 / 41863370 (99.645929%)|0.017379|0.007049|0.051824|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.993309|0.978859|0.999999|0.989429|0.999997|0.000003|
|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.853138|0.305446|0.999994|0.652720|0.999917|0.000083|
|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.853138|0.305446|0.999994|0.652720|0.999917|0.000083|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.947717|0.229999|0.999999|0.614999|0.999913|0.000087|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.894406|0.029664|1.000000|0.514832|0.999892|0.000108|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.849058|0.007755|1.000000|0.503877|0.999890|0.000110|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.758678|0.003541|1.000000|0.501770|0.999889|0.000111|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.990385|0.986031|0.981715|0.972447|0.986057|0.986056|
|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.627946|0.449839|0.350441|0.290188|0.510478|0.510450|
|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.627946|0.449839|0.350441|0.290188|0.510478|0.510450|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.583531|0.370163|0.271053|0.227117|0.466876|0.466854|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.130949|0.057424|0.036775|0.029561|0.162886|0.162875|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.037409|0.015370|0.009672|0.007744|0.081145|0.081139|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.017379|0.007049|0.004421|0.003537|0.051829|0.051824|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986029|0.995464|0.993323|0.994392|0.994392|
|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|0.449806|0.981469|0.852556|0.912482|0.912482|
|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.449806|0.981469|0.852556|0.912482|0.912482|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|0.370136|0.993362|0.790500|0.880396|0.880396|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.057417|0.993866|0.756725|0.859234|0.859234|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|0.015368|0.995484|0.723753|0.838145|0.838145|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.007048|0.995676|0.710626|0.829341|0.829341|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|40978337|276044|885033|377860393721|276044 / 377860669765|885033 / 41863370|
|2|SNOWBALL SPANISH DIRECT|PRIMARY_OUTPUT|12787018|2201196|29076352|377858468569|2201196 / 377860669765|29076352 / 41863370|
|3|SNOWBALL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|12787018|2201196|29076352|377858468569|2201196 / 377860669765|29076352 / 41863370|
|4|HUNSPELL SPANISH LUCENE FILTER|PRIMARY_OUTPUT|9628515|531181|32234855|377860138584|531181 / 377860669765|32234855 / 41863370|
|5|SPANISH LUCENE SPANISH LIGHT STEM FILTER|PRIMARY_OUTPUT|1241848|146613|40621522|377860523152|146613 / 377860669765|40621522 / 41863370|
|6|SPANISH LUCENE SPANISH PLURAL STEM FILTER|PRIMARY_OUTPUT|324656|57716|41538714|377860612049|57716 / 377860669765|41538714 / 41863370|
|7|SPANISH LUCENE SPANISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|148226|47148|41715144|377860622617|47148 / 377860669765|41715144 / 41863370|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 377860669765 (0.000000%)|0 / 41863370 (0.000000%)|1.000000|1.000000|1.000000|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.619928|412198 / 377860669765 (0.000109%)|31822108 / 41863370 (76.014205%)|0.600000|0.383864|0.479978|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.960568|0.239858|0.999999|0.619928|0.999915|0.000085|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|0.600000|0.383864|0.282205|0.237519|0.480000|0.479978|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|41863370|0|0|377860669765|0 / 377860669765|0 / 41863370|
|2|HUNSPELL SPANISH LUCENE FILTER|ANY_CANDIDATE|10041262|412198|31822108|377860257567|412198 / 377860669765|31822108 / 41863370|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999998|1255381 / 377860669765 (0.000332%)|0 / 41863370 (0.000000%)|0.976572|0.985228|0.985334|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.619928|878949 / 377860669765 (0.000233%)|31822108 / 41863370 (76.014205%)|0.586905|0.380469|0.469606|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.970885|1.000000|0.999997|0.999998|0.999997|0.000003|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.919512|0.239858|0.999998|0.619928|0.999913|0.000087|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.976572|0.985228|0.994038|0.970885|0.985335|0.985334|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|0.586905|0.380469|0.281467|0.234926|0.469630|0.469606|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|41863370|1255381|0|377859414384|1255381 / 377860669765|0 / 41863370|
|2|HUNSPELL SPANISH LUCENE FILTER|ALL_CANDIDATES|10041262|878949|31822108|377859790816|878949 / 377860669765|31822108 / 41863370|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL SPANISH LUCENE FILTER|412747|118983|347768|17807|2.048262%|5|888962|
|Radixor|885033|276044|979337|42403|4.877434%|21|914127|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `ES_ES`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Swedish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Swedish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `SV_SE` | 12,371 | 110,468 | 24,731 | 85,737 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **110,468**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 502 | 0.454% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 14,268 | 12.916% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 66,796 | 60.466% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 25,745 | 23.305% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 3,157 | 2.858% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 96.713% | 97.407% | 94.307% | Full Radixor dictionary patch-command stemmer. |
| Lucene SwedishMinimalStemFilter | 49.532% | 49.186% | 50.730% | Minimal suffix reducer; narrow baseline, not a full stemmer. |
| Lucene SwedishLightStemFilter | 45.672% | 46.383% | 43.209% | Light suffix stemmer; intentionally narrower than a dictionary-derived stemmer. |
| Official Snowball direct | 40.068% | 37.512% | 48.926% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
| Lucene SnowballFilter | 38.785% | 35.839% | 48.999% | Lucene TokenFilter integration path around the Snowball algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `swedishRadixor` | 5.489 | 0.355 | 64.0 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene SwedishMinimalStemFilter | `swedishLuceneSwedishMinimalStemFilter` | 4.630 | 0.130 | 54.0 | 0.843 | Minimal Swedish suffix reducer. |
| Lucene SwedishLightStemFilter | `swedishLuceneSwedishLightStemFilter` | 4.876 | 0.328 | 56.9 | 0.888 | Light Swedish suffix stemmer. |
| Official Snowball direct | `snowballDirect[SWEDISH]` | 7.517 | 0.072 | 87.7 | 1.370 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[SWEDISH]` | 9.793 | 0.338 | 114.2 | 1.784 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `SV_SE` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/sv_se/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.974636** among 5 deterministic stemmers. The runner-up is `SNOWBALL SWEDISH DIRECT` at 0.807534, a difference of 0.167101. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.974584** among 5 deterministic stemmers. The runner-up is `SNOWBALL SWEDISH DIRECT` at 0.807599, a difference of 0.166985. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.974636|24473 / 4812155436 (0.000509%)|19546 / 385342 (5.072377%)|0.939665|0.943246|0.943260|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.807534|67105 / 4812155436 (0.001394%)|148325 / 385342 (38.491781%)|0.739832|0.687540|0.692339|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.799307|64262 / 4812155436 (0.001335%)|154666 / 385342 (40.137333%)|0.736940|0.678180|0.684227|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.796072|40227 / 4812155436 (0.000836%)|157161 / 385342 (40.784809%)|0.781991|0.698068|0.709491|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.783685|45941 / 4812155436 (0.000955%)|166707 / 385342 (43.262089%)|0.757232|0.672808|0.684713|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.937292|0.949276|0.999995|0.974636|0.999991|0.000009|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.779348|0.615082|0.999986|0.807534|0.999955|0.000045|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.782117|0.598627|0.999987|0.799307|0.999955|0.000045|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.850127|0.592152|0.999992|0.796072|0.999959|0.000041|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.826360|0.567379|0.999990|0.783685|0.999956|0.000044|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.939665|0.943246|0.946855|0.892588|0.943265|0.943260|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.739832|0.687540|0.642152|0.523856|0.692361|0.692339|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.736940|0.678180|0.628098|0.513065|0.684249|0.684227|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.781991|0.698068|0.630412|0.536179|0.709510|0.709491|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.757232|0.672808|0.605321|0.506941|0.684733|0.684713|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.943241|0.992631|0.993395|0.993013|0.993013|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.687518|0.984860|0.942685|0.963311|0.963311|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.678157|0.985207|0.939659|0.961894|0.961894|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.698048|0.988493|0.944582|0.966038|0.966038|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.672787|0.986795|0.942303|0.964036|0.964036|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|365796|24473|19546|4812130963|24473 / 4812155436|19546 / 385342|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|237017|67105|148325|4812088331|67105 / 4812155436|148325 / 385342|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|230676|64262|154666|4812091174|64262 / 4812155436|154666 / 385342|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|228181|40227|157161|4812115209|40227 / 4812155436|157161 / 385342|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|218635|45941|166707|4812109495|45941 / 4812155436|166707 / 385342|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 4812155436 (0.000000%)|0 / 385342 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|385342|0|0|4812155436|0 / 4812155436|0 / 385342|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999995|47848 / 4812155436 (0.000994%)|0 / 385342 (0.000000%)|0.909640|0.941544|0.943152|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.889545|1.000000|0.999990|0.999995|0.999990|0.000010|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.909640|0.941544|0.975768|0.889545|0.943157|0.943152|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|385342|47848|0|4812107588|47848 / 4812155436|0 / 385342|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|19546|24473|23375|5767|5.878216%|5|104148|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **7 result rows**, **5 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.974584|24473 / 4789911577 (0.000511%)|19546 / 384563 (5.082652%)|0.939544|0.943132|0.943146|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.807599|67105 / 4789911577 (0.001401%)|147975 / 384563 (38.478741%)|0.739645|0.687500|0.692274|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.799355|64262 / 4789911577 (0.001342%)|154316 / 384563 (40.127625%)|0.736744|0.678122|0.684143|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.795947|40227 / 4789911577 (0.000840%)|156939 / 384563 (40.809698%)|0.781694|0.697790|0.709212|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.783598|45941 / 4789911577 (0.000959%)|166437 / 384563 (43.279515%)|0.756945|0.672575|0.684469|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.937167|0.949173|0.999995|0.974584|0.999991|0.000009|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.779037|0.615213|0.999986|0.807599|0.999955|0.000045|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.781800|0.598724|0.999987|0.799355|0.999954|0.000046|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.849816|0.591903|0.999992|0.795947|0.999959|0.000041|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.826025|0.567205|0.999990|0.783598|0.999956|0.000044|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.939544|0.943132|0.946748|0.892384|0.943151|0.943146|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.739645|0.687500|0.642223|0.523810|0.692296|0.692274|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.736744|0.678122|0.628142|0.512999|0.684165|0.684143|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.781694|0.697790|0.630152|0.535851|0.709231|0.709212|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.756945|0.672575|0.605126|0.506676|0.684489|0.684469|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.943127|0.992612|0.993378|0.992995|0.992995|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|0.687478|0.984821|0.942695|0.963298|0.963298|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|0.678100|0.985169|0.939661|0.961877|0.961877|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|0.697770|0.988463|0.944528|0.965996|0.965996|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|0.672553|0.986761|0.942265|0.964000|0.964000|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|365017|24473|19546|4789887104|24473 / 4789911577|19546 / 384563|
|2|SNOWBALL SWEDISH DIRECT|PRIMARY_OUTPUT|236588|67105|147975|4789844472|67105 / 4789911577|147975 / 384563|
|3|SNOWBALL SWEDISH LUCENE FILTER|PRIMARY_OUTPUT|230247|64262|154316|4789847315|64262 / 4789911577|154316 / 384563|
|4|SWEDISH LUCENE SWEDISH MINIMAL STEM FILTER|PRIMARY_OUTPUT|227624|40227|156939|4789871350|40227 / 4789911577|156939 / 384563|
|5|SWEDISH LUCENE SWEDISH LIGHT STEM FILTER|PRIMARY_OUTPUT|218126|45941|166437|4789865636|45941 / 4789911577|166437 / 384563|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 4789911577 (0.000000%)|0 / 384563 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|384563|0|0|4789911577|0 / 4789911577|0 / 384563|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999995|47848 / 4789911577 (0.000999%)|0 / 384563 (0.000000%)|0.909473|0.941433|0.943047|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.889346|1.000000|0.999990|0.999995|0.999990|0.000010|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.909473|0.941433|0.975720|0.889346|0.943051|0.943047|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|384563|47848|0|4789863729|47848 / 4789911577|0 / 384563|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|19546|24473|23375|5767|5.891848%|5|103921|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `SV_SE`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Ukrainian Stemmer Benchmarks
This page reports same-language stemming benchmarks for Ukrainian. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `UK_UA` | 1,493 | 15,737 | 2,985 | 12,752 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **15,737**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `AppendCharacterCommand` | Appends one character to the end of the word form. | 249 | 1.582% |
| `BackwardCompoundCommand` | Applies a multi-step backward patch made from skip, delete, insert, and replace operations. | 4,160 | 26.435% |
| `DeleteSuffixCommand` | Deletes one or more trailing characters from the word form. | 5,859 | 37.231% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 3,004 | 19.089% |
| `ReplaceLastCharacterCommand` | Replaces the final character of the word form. | 2,465 | 15.664% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 99.307% | 99.365% | 99.062% | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | 86.815% | 83.759% | 99.866% | Benchmark-only Ukrainian Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Lucene MorfologikFilter | 92.362% | 90.637% | 99.732% | Dictionary-based path; Morfologik can emit multiple terms. |
| Morfologik direct | 92.362% | 90.637% | 99.732% | Direct dictionary lookup; first returned stem is used for quality when no ranking weight is exposed. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `ukrainianRadixor` | 0.682 | 0.057 | 53.5 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Lucene HunspellStemFilter | `luceneHunspellStemFilter` | 43.527 | 1.207 | 3413.3 | 63.799 | Benchmark-only Ukrainian Hunspell dictionary compared via Lucene HunspellStemFilter. |
| Morfologik direct | `ukrainianMorfologikDirect` | 8.680 | 0.073 | 680.7 | 12.723 | Direct Morfologik dictionary lookup; first returned stem is used for quality. |
| Lucene MorfologikFilter | `ukrainianLuceneMorfologikFilter` | 14.575 | 0.248 | 1143.0 | 21.364 | Dictionary-based Morfologik TokenFilter; may emit multiple terms. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `UK_UA` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/uk_ua/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.995343** among 4 deterministic stemmers. The runner-up is `UKRAINIAN LUCENE MORFOLOGIK FILTER` at 0.928768, a difference of 0.066575. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.995342** among 4 deterministic stemmers. The runner-up is `UKRAINIAN LUCENE MORFOLOGIK FILTER` at 0.928751, a difference of 0.066591. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **12 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.995343|880 / 101387550 (0.000868%)|608 / 65340 (0.930517%)|0.987406|0.988637|0.988632|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.928768|828 / 101387550 (0.000817%)|9308 / 65340 (14.245485%)|0.956896|0.917054|0.919223|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.928646|828 / 101387550 (0.000817%)|9324 / 65340 (14.269972%)|0.956832|0.916912|0.919090|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.885793|794 / 101387550 (0.000783%)|14924 / 65340 (22.840526%)|0.933008|0.865139|0.871499|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986588|0.990695|0.999991|0.995343|0.999985|0.000015|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.985438|0.857545|0.999992|0.928768|0.999900|0.000100|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.985434|0.857300|0.999992|0.928646|0.999900|0.000100|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.984495|0.771595|0.999992|0.885793|0.999845|0.000155|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987406|0.988637|0.989871|0.977529|0.988639|0.988632|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.956896|0.917054|0.880397|0.846814|0.919270|0.919223|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.956832|0.916912|0.880190|0.846572|0.919137|0.919090|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.933008|0.865139|0.806475|0.762331|0.871568|0.871499|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988630|0.997994|0.998266|0.998130|0.998130|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.917004|0.997990|0.971000|0.984310|0.984310|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.916862|0.997990|0.970876|0.984246|0.984246|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.865063|0.998114|0.949804|0.973360|0.973360|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|64732|880|608|101386670|880 / 101387550|608 / 65340|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|56032|828|9308|101386722|828 / 101387550|9308 / 65340|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|56016|828|9324|101386722|828 / 101387550|9324 / 65340|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|50416|794|14924|101386756|794 / 101387550|14924 / 65340|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 101387550 (0.000000%)|0 / 65340 (0.000000%)|1.000000|1.000000|1.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.962151|122 / 101387550 (0.000120%)|4946 / 65340 (7.569636%)|0.982323|0.959732|0.960413|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.962029|122 / 101387550 (0.000120%)|4962 / 65340 (7.594123%)|0.982267|0.959599|0.960286|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.927570|326 / 101387550 (0.000322%)|9465 / 65340 (14.485767%)|0.962884|0.919443|0.922008|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.997984|0.924304|0.999999|0.962151|0.999950|0.000050|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.997983|0.924059|0.999999|0.962029|0.999950|0.000050|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.994199|0.855142|0.999997|0.927570|0.999903|0.000097|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.982323|0.959732|0.938156|0.922581|0.960438|0.960413|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.982267|0.959599|0.937954|0.922337|0.960310|0.960286|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.962884|0.919443|0.879752|0.850897|0.922053|0.922008|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|65340|0|0|101387550|0 / 101387550|0 / 65340|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|60394|122|4946|101387428|122 / 101387550|4946 / 65340|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|60378|122|4962|101387428|122 / 101387550|4962 / 65340|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|55875|326|9465|101387224|326 / 101387550|9465 / 65340|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999993|1490 / 101387550 (0.001470%)|0 / 65340 (0.000000%)|0.982084|0.988727|0.988782|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.962145|1368 / 101387550 (0.001349%)|4946 / 65340 (7.569636%)|0.966650|0.950323|0.950669|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.962023|1368 / 101387550 (0.001349%)|4962 / 65340 (7.594123%)|0.966592|0.950191|0.950541|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.927565|1271 / 101387550 (0.001254%)|9465 / 65340 (14.485767%)|0.950501|0.912349|0.914347|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.977705|1.000000|0.999985|0.999993|0.999985|0.000015|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.977850|0.924304|0.999987|0.962145|0.999938|0.000062|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.977845|0.924059|0.999987|0.962023|0.999938|0.000062|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.977759|0.855142|0.999987|0.927565|0.999894|0.000106|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.982084|0.988727|0.995460|0.977705|0.988789|0.988782|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.966650|0.950323|0.934539|0.905349|0.950700|0.950669|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.966592|0.950191|0.934337|0.905109|0.950571|0.950541|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.950501|0.912349|0.877142|0.838825|0.914398|0.914347|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|65340|1490|0|101386060|1490 / 101387550|0 / 65340|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|60394|1368|4946|101386182|1368 / 101387550|4946 / 65340|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|60378|1368|4962|101386182|1368 / 101387550|4962 / 65340|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|55875|1271|9465|101386279|1271 / 101387550|9465 / 65340|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL UKRAINIAN LUCENE FILTER|5459|468|477|1322|9.280449%|6|15740|
|UKRAINIAN LUCENE MORFOLOGIK FILTER|4362|706|540|2207|15.493155%|6|16937|
|UKRAINIAN MORFOLOGIK DIRECT|4362|706|540|2207|15.493155%|6|16937|
|Radixor|608|880|610|190|1.333801%|2|14435|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **12 result rows**, **4 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.995342|880 / 101259406 (0.000869%)|608 / 65324 (0.930745%)|0.987403|0.988634|0.988629|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.928751|828 / 101259406 (0.000818%)|9308 / 65324 (14.248974%)|0.956884|0.917032|0.919202|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.928751|828 / 101259406 (0.000818%)|9308 / 65324 (14.248974%)|0.956884|0.917032|0.919202|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.885796|794 / 101259406 (0.000784%)|14920 / 65324 (22.839998%)|0.933007|0.865141|0.871500|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.986585|0.990693|0.999991|0.995342|0.999985|0.000015|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.985434|0.857510|0.999992|0.928751|0.999900|0.000100|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.985434|0.857510|0.999992|0.928751|0.999900|0.000100|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.984492|0.771600|0.999992|0.885796|0.999845|0.000155|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.987403|0.988634|0.989868|0.977524|0.988636|0.988629|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.956884|0.917032|0.880367|0.846777|0.919249|0.919202|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.956884|0.917032|0.880367|0.846777|0.919249|0.919202|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.933007|0.865141|0.806479|0.762334|0.871570|0.871500|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988627|0.997992|0.998264|0.998128|0.998128|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|0.916982|0.997988|0.970978|0.984298|0.984298|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|0.916982|0.997988|0.970978|0.984298|0.984298|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|0.865065|0.998113|0.949788|0.973351|0.973351|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|64716|880|608|101258526|880 / 101259406|608 / 65324|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|PRIMARY_OUTPUT|56016|828|9308|101258578|828 / 101259406|9308 / 65324|
|3|UKRAINIAN MORFOLOGIK DIRECT|PRIMARY_OUTPUT|56016|828|9308|101258578|828 / 101259406|9308 / 65324|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|PRIMARY_OUTPUT|50404|794|14920|101258612|794 / 101259406|14920 / 65324|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 101259406 (0.000000%)|0 / 65324 (0.000000%)|1.000000|1.000000|1.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.962142|122 / 101259406 (0.000120%)|4946 / 65324 (7.571490%)|0.982318|0.959722|0.960404|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.962142|122 / 101259406 (0.000120%)|4946 / 65324 (7.571490%)|0.982318|0.959722|0.960404|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.927552|326 / 101259406 (0.000322%)|9465 / 65324 (14.489315%)|0.962874|0.919422|0.921988|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.997983|0.924285|0.999999|0.962142|0.999950|0.000050|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.997983|0.924285|0.999999|0.962142|0.999950|0.000050|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.994198|0.855107|0.999997|0.927552|0.999903|0.000097|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|0.982318|0.959722|0.938141|0.922562|0.960428|0.960404|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|0.982318|0.959722|0.938141|0.922562|0.960428|0.960404|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|0.962874|0.919422|0.879722|0.850861|0.922033|0.921988|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|65324|0|0|101259406|0 / 101259406|0 / 65324|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ANY_CANDIDATE|60378|122|4946|101259284|122 / 101259406|4946 / 65324|
|3|UKRAINIAN MORFOLOGIK DIRECT|ANY_CANDIDATE|60378|122|4946|101259284|122 / 101259406|4946 / 65324|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ANY_CANDIDATE|55859|326|9465|101259080|326 / 101259406|9465 / 65324|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999993|1490 / 101259406 (0.001471%)|0 / 65324 (0.000000%)|0.982079|0.988724|0.988779|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.962136|1368 / 101259406 (0.001351%)|4946 / 65324 (7.571490%)|0.966642|0.950311|0.950657|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.962136|1368 / 101259406 (0.001351%)|4946 / 65324 (7.571490%)|0.966642|0.950311|0.950657|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.927547|1271 / 101259406 (0.001255%)|9465 / 65324 (14.489315%)|0.950487|0.912326|0.914325|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.977699|1.000000|0.999985|0.999993|0.999985|0.000015|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.977845|0.924285|0.999986|0.962136|0.999938|0.000062|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.977845|0.924285|0.999986|0.962136|0.999938|0.000062|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.977752|0.855107|0.999987|0.927547|0.999894|0.000106|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.982079|0.988724|0.995459|0.977699|0.988787|0.988779|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|0.966642|0.950311|0.934522|0.905326|0.950688|0.950657|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|0.966642|0.950311|0.934522|0.905326|0.950688|0.950657|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|0.950487|0.912326|0.877111|0.838787|0.914376|0.914325|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|65324|1490|0|101257916|1490 / 101259406|0 / 65324|
|2|UKRAINIAN LUCENE MORFOLOGIK FILTER|ALL_CANDIDATES|60378|1368|4946|101258038|1368 / 101259406|4946 / 65324|
|3|UKRAINIAN MORFOLOGIK DIRECT|ALL_CANDIDATES|60378|1368|4946|101258038|1368 / 101259406|4946 / 65324|
|4|HUNSPELL UKRAINIAN LUCENE FILTER|ALL_CANDIDATES|55859|1271|9465|101258135|1271 / 101259406|9465 / 65324|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|HUNSPELL UKRAINIAN LUCENE FILTER|5455|468|477|1321|9.279292%|6|15730|
|UKRAINIAN LUCENE MORFOLOGIK FILTER|4362|706|540|2207|15.502950%|6|16928|
|UKRAINIAN MORFOLOGIK DIRECT|4362|706|540|2207|15.502950%|6|16928|
|Radixor|608|880|610|190|1.334645%|2|14426|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `UK_UA`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Yiddish Stemmer Benchmarks
This page reports same-language stemming benchmarks for Yiddish. Accuracy is listed first because speed without root agreement is not enough to interpret stemmer quality.
All speed values are environment-specific and were measured on the hardware and JVM listed in the [benchmark overview](../index.md). Speed benchmark operations process changed dictionary tokens only. Accuracy uses the complete Radixor dictionary for the language.
Radixor must not be read as simply "slower" when a narrow competitor has a lower timing row. In these tables Radixor is the quality-oriented baseline: its exact-root accuracy is typically close to 100%, while many faster rule-based, light, minimal, or possessive filters reach that speed by doing much less linguistic work and often score far lower in `All exact` and `Changed exact`. The Radixor rows in this benchmark refresh use the contracted compiled patch trie: compilation collapses uniform patch-command subtrees into accepting leaves, reducing hot lookup depth while preserving the preferred stemming result measured by the accuracy pass. The [EnglishRadixorDictionaryCoverageBenchmark](../reference/english-coverage.md) table shows the resulting quality/speed envelope explicitly. The same interpretation applies to this language page: speed rows must be read together with the accuracy table above them.
## Dictionary Corpus
| Resource | Dictionary rows | Complete quality tokens | Already-root tokens | Changed speed tokens |
| --- | ---: | ---: | ---: | ---: |
| `YI` | 802 | 4,300 | 1,524 | 2,776 |
## Radixor Patch Command Distribution
Radixor stores the preferred transformation for each normalized dictionary word form as a compiled patch command. This distribution shows which runtime command class is selected by the trained trie for the complete language dictionary. The total number of preferred patch commands analyzed for this language is **4,300**.
| Command class | Meaning | Word forms | Share |
| --- | --- | ---: | ---: |
| `DeletePrefixCommand` | Deletes one or more leading characters from the word form in forward traversal. | 25 | 0.581% |
| `ForwardCompoundCommand` | Applies a multi-step forward patch made from skip, delete, insert, and replace operations. | 2,721 | 63.279% |
| `PreserveCommand` | Returns the word form unchanged because it already matches the preferred root. | 1,551 | 36.070% |
| `ReplaceFirstCharacterCommand` | Replaces the first character of the word form in forward traversal. | 3 | 0.070% |
## Accuracy
Accuracy is computed from JMH auxiliary counters in the current report. The counters are deterministic for a fixed corpus and stemmer; percentages divide matching counters by evaluated counters from the same report and are not timing metrics.
| Stemmer | All exact | Changed exact | Root preserved | Note |
| --- | ---: | ---: | ---: | --- |
| Radixor | 98.930% | 98.343% | 100.000% | Full Radixor dictionary patch-command stemmer. |
| Lucene SnowballFilter | 2.837% | 2.558% | 3.346% | Lucene TokenFilter integration path around the Snowball algorithm. |
| Official Snowball direct | 2.837% | 2.558% | 3.346% | Official Snowball generated Java stemmer; rule-based suffix algorithm. |
## Speed
Speed uses JMH average time, 3 warmup iterations, 5 measurement iterations, 1 fork, and 1 thread. Relative factor is computed against the single Radixor row on this language page. Values below 1.000 are faster than that Radixor baseline; values above 1.000 are slower.
| Stemmer | Benchmark method | Score ms/op | Error ms | ns/token | Relative vs Radixor | Note |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Radixor | `radixor[YIDDISH]` | 0.254 | 0.004 | 50.7 | 1.000 | Full Radixor dictionary patch-command stemmer. |
| Official Snowball direct | `snowballDirect[YIDDISH]` | 1.537 | 0.220 | 307.3 | 6.058 | Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter | `luceneSnowballFilter[YIDDISH]` | 1.714 | 0.120 | 342.8 | 6.756 | Lucene TokenFilter path around Snowball; includes TokenStream overhead. |
## Interpretation Notes
- Radixor is a dictionary-derived patch-command stemmer. Its quality depends on the language resource used to train the compiled trie.
- Light, minimal, plural, and possessive filters are narrow baselines. They can be fast because they intentionally perform less linguistic work.
- Lucene TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
- Morfologik rows are dictionary-based and can emit multiple terms for one input token. Quality rows use the first returned term when no ranking weight is available.
- Snowball rows are rule-based generated suffix stemmers; they are useful algorithmic baselines, not dictionary-root equivalence guarantees.
<!-- STEMMING-QUALITY:START -->
## Stemming Quality
Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language `YI` using the complete validated stemming-quality result matrix. Every usable dictionary row is one gold-standard group of forms expected to share a morphological family or lemma. Exact equality with a predetermined lemma is not required. Same-row pairs are positive pairs; pairs from different rows are negative pairs.
`ALL_WORDS` includes every valid group and its original forms. `LOWERCASE_GROUPS_ONLY` excludes an entire group when any Unicode code point is uppercase or titlecase; retained words are not lowercased or otherwise rewritten. This isolates case-handling effects without changing retained inputs. [Download the complete machine-readable result snapshot](../data/stemming-quality.csv).
### Evaluation Scope and Key Findings
The dictionary resource is `src/main/resources/yi/stemmer.gz`. The following findings compare only deterministic `PRIMARY_OUTPUT` rows over identical included groups; candidate policies are reported separately as capability analyses.
- **ALL_WORDS:** `Radixor` ranks first by balanced accuracy at **0.988241** among 3 deterministic stemmers. The runner-up is `SNOWBALL YIDDISH DIRECT` at 0.890988, a difference of 0.097253. This rank does not imply leadership in throughput or every secondary metric.
- **LOWERCASE_GROUPS_ONLY:** `Radixor` ranks first by balanced accuracy at **0.988241** among 3 deterministic stemmers. The runner-up is `SNOWBALL YIDDISH DIRECT` at 0.890988, a difference of 0.097253. This rank does not imply leadership in throughput or every secondary metric.
### `ALL_WORDS`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988241|195 / 6392909 (0.003050%)|149 / 6344 (2.348676%)|0.970881|0.972986|0.972965|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.969484|0.976513|0.999969|0.988241|0.999946|0.000054|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.970881|0.972986|0.975099|0.947393|0.972992|0.972965|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.972959|0.995691|0.996142|0.995917|0.995917|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|6195|195|149|6392714|195 / 6392909|149 / 6344|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6392909 (0.000000%)|0 / 6344 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|6344|0|0|6392909|0 / 6392909|0 / 6344|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999970|389 / 6392909 (0.006085%)|0 / 6344 (0.000000%)|0.953240|0.970253|0.970653|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.942225|1.000000|0.999939|0.999970|0.999939|0.000061|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.953240|0.970253|0.987885|0.942225|0.970683|0.970653|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|6344|389|0|6392520|389 / 6392909|0 / 6344|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|149|195|194|89|2.487423%|3|3676|
### `LOWERCASE_GROUPS_ONLY`
This mode contains **5 result rows**, **3 evaluated stemmers**, and **3 output policies**. Applied-row and form counts are shown per row because adapters share the language corpus but policy rows remain independently auditable. Rankings are separated by output policy and ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. Balanced accuracy is a navigation metric, not a universally authoritative quality score.
#### `PRIMARY_OUTPUT` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.988241|195 / 6392909 (0.003050%)|149 / 6344 (2.348676%)|0.970881|0.972986|0.972965|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.890988|1151 / 6392909 (0.018004%)|1382 / 6344 (21.784363%)|0.805624|0.796661|0.796600|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.969484|0.976513|0.999969|0.988241|0.999946|0.000054|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.811713|0.782156|0.999820|0.890988|0.999604|0.000396|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.970881|0.972986|0.975099|0.947393|0.972992|0.972965|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.805624|0.796661|0.787894|0.662041|0.796798|0.796600|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|0.972959|0.995691|0.996142|0.995917|0.995917|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|0.796462|0.982919|0.962014|0.972354|0.972354|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|PRIMARY_OUTPUT|6195|195|149|6392714|195 / 6392909|149 / 6344|
|2|SNOWBALL YIDDISH DIRECT|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
|3|SNOWBALL YIDDISH LUCENE FILTER|PRIMARY_OUTPUT|4962|1151|1382|6391758|1151 / 6392909|1382 / 6344|
</details>
#### `ANY_CANDIDATE` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|0 / 6392909 (0.000000%)|0 / 6344 (0.000000%)|1.000000|1.000000|1.000000|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|0.000000|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|1.000000|1.000000|1.000000|1.000000|1.000000|1.000000|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ANY_CANDIDATE|6344|0|0|6392909|0 / 6392909|0 / 6344|
</details>
#### `ALL_CANDIDATES` ranking
<div class="quality-table quality-table--compact" role="region" aria-label="Compact stemming-quality ranking; scroll horizontally for additional columns" tabindex="0" markdown="1">
| Rank | Stemmer | Output policy | Balanced accuracy | Over-stemming | Under-stemming | F0.5 | F1 | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.999970|389 / 6392909 (0.006085%)|0 / 6344 (0.000000%)|0.953240|0.970253|0.970653|
</div>
<details class="quality-details" markdown="1"><summary>Classification metrics</summary>
| Rank | Stemmer | Output policy | Precision | Recall | Specificity | Balanced accuracy | Pairwise accuracy | Error rate |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.942225|1.000000|0.999939|0.999970|0.999939|0.000061|
</details>
<details class="quality-details" markdown="1"><summary>Pair-relation metrics</summary>
| Rank | Stemmer | Output policy | F0.5 | F1 | F2 | Jaccard | FowlkesMallows | MCC |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|0.953240|0.970253|0.987885|0.942225|0.970683|0.970653|
</details>
<details class="quality-details" markdown="1"><summary>Partition metrics (PRIMARY_OUTPUT only)</summary>
| Rank | Stemmer | Output policy | Adjusted Rand Index | Homogeneity | Completeness | V-measure | Normalized mutual information |
|---:|---|---|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|n/a|n/a|n/a|n/a|n/a|
</details>
<details class="quality-details" markdown="1"><summary>Raw pair counts</summary>
| Rank | Stemmer | Output policy | TP | FP | FN | TN | Over error / possible | Under error / possible |
|---:|---|---|---:|---:|---:|---:|---:|---:|
|1|Radixor|ALL_CANDIDATES|6344|389|0|6392520|389 / 6392909|0 / 6344|
</details>
#### Multi-output analysis
Alternative candidates are capability analyses, not replacements for the deterministic comparison.
| Stemmer | Under pairs repaired | Best-case over pairs avoided | All-candidate collisions added | Multi-candidate forms | Multi-candidate share | Maximum candidates | Total candidate assignments |
|---|---:|---:|---:|---:|---:|---:|---:|
|Radixor|149|195|194|89|2.487423%|3|3676|
### Output Policies and Metric Definitions
`PRIMARY_OUTPUT` uses one deterministic stem per form and therefore defines a strict partition. `ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound: a same-group pair succeeds when candidates intersect, while a different-group pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and need not represent one globally consistent assignment. `ALL_CANDIDATES` activates every returned candidate; forms are related when candidate sets intersect. Alternatives can reduce under-stemming but can introduce cross-group collisions, and the resulting relation can overlap and need not be a partition.
For each row, `TP = underPossiblePairs - underErrorPairs`, `FN = underErrorPairs`, `FP = overErrorPairs`, and `TN = overPossiblePairs - overErrorPairs`. TP and FN concern same-group pairs; FP and TN concern different-group pairs. Consequently, under-stemming and over-stemming use different denominators. Undefined values are rendered as `n/a`.
- Under-stemming rate: `FN / (TP + FN)`, the false-negative rate over same-group pairs.
- Over-stemming rate: `FP / (TN + FP)`, the false-positive rate over different-group pairs.
- Pairwise precision: `TP / (TP + FP)`, the fraction of predicted conflations that are gold-standard positive pairs.
- Pairwise recall: `TP / (TP + FN)`, the fraction of gold-standard positive pairs successfully connected.
- Pairwise specificity: `TN / (TN + FP)`, the fraction of different-group pairs correctly separated.
- Balanced accuracy: `(recall + specificity) / 2`. It gives equal weight to positive and negative pair classes and is less dominated by the large true-negative class than ordinary accuracy. It does not replace the raw errors or other metrics.
- Pairwise F-beta: `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`. F0.5 emphasizes precision and penalizes over-stemming more; F1 weights precision and recall equally; F2 emphasizes recall and penalizes under-stemming more.
- MCC: `(TP * TN - FP * FN) / sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))`. It uses all confusion counts and remains useful under class imbalance, except when its denominator is degenerate.
- Jaccard index: `TP / (TP + FP + FN)`.
- FowlkesMallows index: `sqrt(precision * recall)`.
- Pairwise accuracy: `(TP + TN) / (TP + TN + FP + FN)`. It can be dominated by true-negative cross-group pairs.
- Pairwise error rate: `(FP + FN) / (TP + TN + FP + FN)`.
Adjusted Rand Index uses the gold/predicted contingency table and chance correction. Homogeneity is `1 - H(gold | predicted) / H(gold)`; completeness is `1 - H(predicted | gold) / H(predicted)`; V-measure is their harmonic mean; normalized mutual information uses the arithmetic-mean entropy normalization `MI / ((H(gold) + H(predicted)) / 2)`. These partition-only metrics apply to `PRIMARY_OUTPUT`; candidate-relation rows show `n/a`.
### Provenance
- Authoritative source: `docs/benchmarks/data/stemming-quality.csv`
- Source SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Evaluation command: `./gradlew stemmingQuality`
- Dictionary language: `YI`
- Processing modes: `ALL_WORDS`, `LOWERCASE_GROUPS_ONLY`
- Stemmer versions and transitive artifacts: resolved by the repository's JMH Gradle configuration and `gradle.lockfile`
- Radixor version, Git revision, generation date, JDK version, operating system, and dictionary revision: not recorded in the authoritative CSV
<!-- STEMMING-QUALITY:END -->

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# Benchmark Candidate Evaluation
Implemented benchmark methods are documented on the per-language pages under [Language Benchmark Pages](../languages/index.md). This keeps the exact method names, implementation descriptions, accuracy tables, and speed tables close to the language where they are valid.
## Included Candidate Families
The current benchmark pages include Radixor baselines, Lucene language filters where the language matches a bundled Radixor resource, Lucene Stempel and Morfologik paths where applicable, official Snowball Java stemmers where same-language comparison is available, benchmark-only CISTEM German stemmer evaluation, benchmark-only Hunspell comparisons, and selected English-specific non-Lucene baselines such as OpenNLP Porter and Paice/Husk Lancaster.
Benchmark-only Hunspell comparisons use bundled benchmark dictionaries and the Lucene HunspellStemFilter adapter over the selected language token streams.
The CISTEM candidate is implemented in `src/jmh/java/org/egothor/stemmer/benchmark/Cistem.java` and follows the original MIT-licensed upstream implementation from Leonie Weissweiler's CISTEM project.
CISTEM German gold-standard files are not vendored in this repository. The Gradle JMH resource preparation tasks download `goldstandard1.txt` and `goldstandard2.txt` from the upstream CISTEM repository into generated build resources.
Direct stemmer APIs and Lucene TokenFilter paths are documented separately on language pages. TokenFilter rows include TokenStream, attribute, and required normalization overhead. Direct rows measure exposed direct APIs.
For the benchmark refresh used in this documentation build:
- Hunspell families are included in `HunspellStemmerComparisonBenchmark` (speed) and `HunspellStemmerComparisonBenchmarkQuality` (quality for all benchmark languages in this corpus). The legacy
`EnglishHunspellStemmerComparisonBenchmarkQuality` result is retained for continuity.
- CISTEM quality is present in the published per-language results under `GERMAN_CISTEM`. CISTEM speed is present as `germanCistem` in `MultiLanguageStemmerComparisonBenchmark`.
## Evaluated But Skipped Candidates
| Candidate | Language | Link/source | Reason skipped |
| --- | --- | --- | --- |
| Lucene Arabic, Bulgarian, Bengali, Sorani, Greek, Galician, Hindi, Indonesian, Latvian, Telugu filters | Various | `lucene-analysis-common` | No bundled same-language Radixor resource in this repository snapshot. |
| Lucene analyzer-only paths | Multiple | Lucene analyzers | Full analyzers mix tokenization, stop-word handling, and other behavior; direct filters are used where available. |
| Lucene StemmerOverrideFilter | Multiple | `lucene-analysis-common` | Override map facility, not a stemmer algorithm. |
| Additional Snowball Lovins | English | Official Snowball Java distribution | No Lovins Java stemmer was present in the selected Snowball Java distribution. |
| Lemur Project Krovetz Stemmer | English | Lemur Project | Lucene KStem represents the Krovetz-style path without adding separate dependency and license risk. |
| Smile Lancaster / Paice-Husk | English | Smile NLP | Smile is large for one stemmer; Paice/Husk is included through a smaller benchmark-only generated path. |
| `stemmerEval` reference repository | Multiple | `https://github.com/endredy/stemmerEval` | Used only as a candidate reference; no code or data copied. |

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# Benchmark Corpora
The table below describes the Radixor resources used to build speed and quality corpora. `Total tokens` is the complete dictionary token count used by quality benchmarks. `Already-root tokens` counts fields where the token is already equal to the line root. `Changed tokens` is the speed workload before the minimum-size repeat rule.
| Language resource | Dictionary rows | Total tokens | Already-root tokens | Changed tokens | Speed timing tokens |
| --- | ---: | ---: | ---: | ---: | ---: |
| `cs_cz` | 5,113 | 56,612 | 10,049 | 46,563 | 46,563 |
| `da_dk` | 4,179 | 32,256 | 8,356 | 23,900 | 23,900 |
| `de_de` | 39,315 | 213,440 | 73,799 | 139,641 | 139,641 |
| `es_es` | 65,059 | 926,393 | 120,121 | 806,272 | 806,272 |
| `fa_ir` | 69 | 3,770 | 138 | 3,632 | 5,000 |
| `fi_fi` | 57,027 | 1,865,215 | 110,525 | 1,754,690 | 1,754,690 |
| `fr_fr` | 59,240 | 474,110 | 108,141 | 365,969 | 365,969 |
| `he_il` | 2,358 | 61,071 | 4,715 | 56,356 | 56,356 |
| `hu_hu` | 19,406 | 935,713 | 38,775 | 896,938 | 896,938 |
| `it_it` | 10,009 | 337,546 | 20,004 | 317,542 | 317,542 |
| `nb_no` | 17,929 | 90,757 | 33,376 | 57,381 | 57,381 |
| `nl_nl` | 4,992 | 31,466 | 9,981 | 21,485 | 21,485 |
| `nn_no` | 4,688 | 19,651 | 6,089 | 13,562 | 13,562 |
| `pl_pl` | 9,990 | 132,308 | 19,957 | 112,351 | 112,351 |
| `pt_pt` | 4,001 | 215,490 | 8,002 | 207,488 | 207,488 |
| `ru_ru` | 37,410 | 806,279 | 74,808 | 731,471 | 731,471 |
| `sv_se` | 12,371 | 110,468 | 24,731 | 85,737 | 85,737 |
| `uk_ua` | 1,493 | 15,737 | 2,985 | 12,752 | 12,752 |
| `us_uk` | 396,939 | 1,004,374 | 793,874 | 210,500 | 210,500 |
| `yi` | 802 | 4,300 | 1,524 | 2,776 | 5,000 |
Speed benchmarks process the complete changed-token dictionary sequence for the language. Only resources with fewer than 5,000 changed tokens are repeated to reach the minimum timing size; larger resources are not sampled or truncated.

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# English Dictionary Coverage Benchmark
`EnglishRadixorDictionaryCoverageBenchmark` builds Radixor from deterministic slices of the English dictionary rows and evaluates accuracy against the complete dictionary. The speed method then stems the full changed-token English timing corpus.
This benchmark is the clearest demonstration of the Radixor quality/speed envelope after contracted-trie compilation. More dictionary knowledge still gives the strongest changed-form precision, but uniform-subtree contraction removes much of the historical lookup-depth penalty. The table should therefore be read as a measured operating curve rather than as a strictly monotonic function of dictionary size.
| Used rows | Actual row ratio | All exact | Changed exact | Root preserved | Speed ms/op | Error ms | ns/token |
| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| 100% | 100.000% | 97.478% | 97.197% | 97.552% | 28.578 | 7.571 | 135.8 |
| 90% | 90.000% | 97.047% | 94.913% | 97.613% | 26.612 | 9.227 | 126.4 |
| 80% | 80.000% | 96.635% | 92.768% | 97.661% | 23.331 | 8.106 | 110.8 |
| 70% | 70.000% | 96.209% | 90.565% | 97.705% | 22.362 | 1.957 | 106.2 |
| 60% | 60.000% | 95.750% | 88.384% | 97.703% | 16.497 | 2.026 | 78.4 |
| 50% | 50.000% | 95.262% | 86.107% | 97.690% | 16.035 | 0.986 | 76.2 |
| 40% | 40.000% | 94.753% | 83.855% | 97.643% | 16.459 | 0.664 | 78.2 |
| 30% | 30.000% | 94.208% | 81.651% | 97.537% | 19.566 | 0.758 | 92.9 |
| 20% | 20.000% | 93.633% | 79.366% | 97.416% | 14.616 | 0.487 | 69.4 |
| 10% | 10.000% | 92.868% | 76.516% | 97.204% | 18.093 | 3.147 | 86.0 |
## Column Meanings
- `Used rows`: requested deterministic percentage of English dictionary rows used to build the trie.
- `Actual row ratio`: selected rows divided by all parsed English dictionary rows.
- `All exact`: exact agreement over the complete dictionary.
- `Changed exact`: exact agreement over dictionary tokens where `token != expectedRoot`.
- `Root preserved`: percentage of already-root dictionary tokens that are left unchanged.
- `Speed ms/op`: JMH average time for one full changed-token English operation.
- `Error ms`: JMH score error converted to milliseconds.
- `ns/token`: `Speed ms/op` divided by 210,500 changed English tokens.
For non-English languages, the same principle applies: dictionary-driven Radixor quality depends on the amount and consistency of the language resource, while contracted tries reduce the cost of uniform regions in the compiled lookup graph. The English table is the clearest because the English resource is large and the benchmark can show gradual deterministic reductions from 100% to 10%.
## Why The Historical Porter Ratio Changed
The historical English benchmark in `HEAD` used synthetic lexical families. Its `familyCount=5000` parameter generated roughly 70,000 artificial tokens rather than measuring the complete real English dictionary resource. That older workload was useful as a low-level stress test, but it was not a dictionary-quality comparison. Many synthetic tokens were not present in the Radixor dictionary, so Radixor often executed a fast miss path where lookup returned `null` and no patch command was applied.
The current benchmark is intentionally based on real Radixor dictionary data. For English, the speed workload processes 210,500 changed token/root pairs where the dictionary token differs from the expected root, and the quality workload evaluates the complete 1,004,374-token dictionary. This is a hit-heavy workload that measures real lookup plus compiled patch-command application against known expected roots. It is therefore a different and more linguistically meaningful workload than the historical synthetic benchmark.
The result must be interpreted in Radixor's favor through both speed and exact-root quality. Non-Radixor stemmers can look faster because many of them perform narrower or more aggressive transformations and do not attempt to match the dictionary root with the same precision. The English result table shows that this speed often comes with substantially lower `All exact` and `Changed exact` accuracy.
Radixor uses the dictionary as training data for transformation rules. With the full English dictionary, it reaches much higher exact-root agreement than the Porter-family and other narrow baselines. Higher speed is still possible by reducing the amount or complexity of the input dictionary used to build the stemmer, but that is an explicit quality/speed trade-off rather than an accidental benchmark artifact.
The coverage table shows that contracted tries substantially improve the operating point. Reducing dictionary knowledge still primarily damages changed-form exactness, while root preservation remains high. Even when Radixor is trained from only 10% of the English dictionary rows, the complete-dictionary `All exact` score remains above 92%. This is why Radixor performance should be discussed as a configurable quality/speed point, not as a single fixed ratio against Porter.

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# Benchmark Environment And Reports
The values below are environment-specific and must not be read as universal performance claims.
| Item | Value |
| --- | --- |
| Benchmark date | 2026-07-06 (Europe/Prague) |
| Focused comparison command family | `./gradlew jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' --no-daemon` |
| English coverage command | `./gradlew jmh -Pjmh.includes='.*EnglishRadixorDictionaryCoverageBenchmark.*' --no-daemon` |
| Speed result reports | `build/reports/jmh/stemmer-comparison-2026-07-06.csv`, `build/reports/jmh/stemmer-comparison-2026-07-06.txt`, `build/reports/jmh/english-coverage-2026-07-06.csv`, and `build/reports/jmh/english-coverage-2026-07-06.txt` |
| Accuracy result reports | `build/reports/jmh/stemmer-comparison-2026-07-06.csv`, `build/reports/jmh/english-coverage-2026-07-06.csv`, and deterministic Radixor exact-root accounting over the same bundled language corpora |
| Final comparison JMH scope | Stemmer comparison benchmarks only; internal `FrequencyTrie*` microbenchmarks were not run |
| Coverage JMH scope | English Radixor dictionary coverage benchmark only |
| JMH version | 1.37 |
| Speed benchmark mode | Average time, `time/op` |
| Score unit | `ns/op` |
| Speed warmup | 3 iterations, 1 s each |
| Speed measurement | 5 iterations, 1 s each |
| Accuracy warmup | 3 JMH warmup iterations were applied by the Gradle invocation; timing scores from quality methods are not interpreted |
| Accuracy measurement | 5 JMH measurement samples; documentation uses deterministic auxiliary counter ratios from the same report |
| Fork count in generated report files | 1 |
| Default fork policy for accuracy-only benchmark classes | `@Fork(0)` for future default runs because accuracy counters are deterministic and not interpreted as speed |
| Thread count | 1 |
| JVM reported by JMH | JDK 25.0.3, OpenJDK 64-Bit Server VM, 25.0.3+9 |
| Java runtime | OpenJDK Runtime Environment, Red Hat build 25.0.3+9 |
| JVM invoker | `/usr/lib/jvm/java-25-openjdk/bin/java` |
| Operating system | Fedora Linux 44 (MATE-Compiz) |
| Kernel | Linux 7.0.12-201.fc44.x86_64 |
| Architecture | x86_64 |
| CPU | AMD Ryzen 5 8600G w/ Radeon 760M Graphics |
| Physical cores | 6 |
| Logical CPUs | 12 |
## Contracted Trie Baseline
All Radixor rows in the refreshed benchmark tables use contracted compiled patch tries. During compilation, a subtree whose reachable entries all resolve to the same preferred patch command is represented as an accepting leaf. Runtime lookup can therefore stop as soon as that leaf is reached, which reduces depth in uniform regions while preserving the preferred result used by `get()`.
## Report Files
Generated local report files for this benchmark update:
- `build/reports/jmh/stemmer-comparison-2026-07-06.csv`
- `build/reports/jmh/stemmer-comparison-2026-07-06.txt`
- `build/reports/jmh/english-coverage-2026-07-06.csv`
- `build/reports/jmh/english-coverage-2026-07-06.txt`
JMH TXT and CSV reports are still published as benchmark artifacts. They are not converted into a Porter speed badge.
## Published Metrics
The historical English Radixor versus Porter performance badge is no longer generated. `tools/generate-pages-badges.py` now produces only coverage and mutation badge endpoint JSON files:
- `coverage-badge.json`
- `pitest-badge.json`
The README therefore no longer presents a single Porter speed ratio. Benchmark interpretation now uses both speed and quality, because a narrow or aggressive stemmer can be fast while disagreeing with the dictionary root much more often than Radixor.

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# Linguistic Quality Methodology
This evaluation measures agreement between the relation predicted by a stemmer and the gold-standard relation represented by Radixor dictionary groups. It does not require a generated stem to equal one predetermined lemma string. Runtime performance and linguistic quality are separate measurements.
## Scope and fair-comparison rules
The authoritative Radixor language universe is the reconciliation of registered default model descriptors and `StemmerPatchTrieLoader.Language`. Radixor is evaluated for every reconciled language. Optional models are separate comparison rows. A third-party adapter is evaluated only for languages supported by its tested implementation and having a compatible Radixor dictionary; unsupported combinations are absent rather than assigned zero quality.
Model identity is part of the candidate identity. Default Polish means `pl-pl-unimorph`; optional PoliMorf means `pl-pl-polimorf`. Results for those inputs must not be combined or relabeled, and historical snapshots cannot acquire a newer model identity retroactively.
Within one language and dictionary mode, every adapter receives the same original included forms. Exact duplicates are removed only within one dictionary row. Identical surface forms in different rows remain distinct entries. Candidate strings use exact `String.equals`, with no evaluation-only lowercasing, normalization, accent removal, or gold-label-aware selection. Adapter preprocessing and lifecycle match the JMH comparison path.
## Gold-standard pairs
Every usable dictionary row is a gold-standard equivalence group. An unordered pair from the same row is positive; a pair from different rows is negative. For group size `n`, `C2(n) = n * (n - 1) / 2`.
- `TP = underPossiblePairs - underErrorPairs`: same-group pairs correctly related.
- `FN = underErrorPairs`: same-group pairs incorrectly separated.
- `FP = overErrorPairs`: different-group pairs incorrectly related.
- `TN = overPossiblePairs - overErrorPairs`: different-group pairs correctly separated.
Under-stemming is the false-negative relation among same-group pairs. Over-stemming is the false-positive relation among different-group pairs. Their percentages use different denominators and must not be added or averaged without an explicitly defined composite.
## Dictionary-processing modes
- `ALL_WORDS` includes every valid group and preserves every original form.
- `LOWERCASE_GROUPS_ONLY` excludes an entire group if any Unicode code point is uppercase or titlecase. Retained forms are not converted to lowercase. Digits, punctuation, combining marks, and characters without case distinctions do not exclude a group by themselves.
## Output policies
`PRIMARY_OUTPUT` uses the adapter's deterministic primary stem. It defines a strict predicted partition and is the principal direct comparison between implementations.
`ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound. Same-group pairs succeed when candidate sets intersect. Different-group pairs avoid an error whenever a non-colliding candidate selection exists. Selection may differ between pairs, so this policy is not deterministic runtime behaviour and may not correspond to one globally realizable assignment.
`ALL_CANDIDATES` treats every returned candidate as active. Two forms are related when their candidate sets intersect. Alternatives can recover same-group relationships while introducing cross-group collisions. This overlapping relation need not be transitive or form a partition.
Candidate-aware policies are reported as capability analyses. They are not mixed into the principal `PRIMARY_OUTPUT` ranking.
## Relation metrics
Undefined denominators produce `n/a`, never zero, `NaN`, or infinity. Metrics are calculated from unrounded raw counts and displayed with six decimals.
| Metric | Formula | Range and interpretation | Sensitivity and applicability |
| --- | --- | --- | --- |
| Under-stemming rate | `FN / (TP + FN)` | `[0, 1]`; lower is better. False-negative rate over same-group pairs. | Sensitive to splitting large gold groups. All policies. |
| Over-stemming rate | `FP / (TN + FP)` | `[0, 1]`; lower is better. False-positive rate over different-group pairs. | The denominator is usually very large. All policies. |
| Precision | `TP / (TP + FP)` | `[0, 1]`; higher is better. Fraction of predicted relations that are gold-positive. | Penalizes over-stemming. All policies, with oracle-assisted interpretation for `ANY_CANDIDATE`. |
| Recall | `TP / (TP + FN)` | `[0, 1]`; higher is better. Fraction of gold-positive pairs recovered. | Equivalent to one minus the under-stemming rate. All policies. |
| Specificity | `TN / (TN + FP)` | `[0, 1]`; higher is better. Fraction of negative pairs separated. | Sensitive to cross-group collisions. All policies. |
| Balanced accuracy | `(recall + specificity) / 2` | `[0, 1]`; higher is better. Equal weight for positive and negative classes. | Primary navigation metric; less dominated by TN than ordinary accuracy, but not uniquely authoritative. |
| Pairwise accuracy | `(TP + TN) / (TP + TN + FP + FN)` | `[0, 1]`; higher is better. | Can be dominated by the very large TN class and is not the default ranking metric. |
| Pairwise error rate | `(FP + FN) / (TP + TN + FP + FN)` | `[0, 1]`; lower is better. | Also sensitive to the number of negative pairs. |
| F0.5 | `1.25 TP / (1.25 TP + 0.25 FN + FP)` | `[0, 1]`; higher is better. | Gives greater weight to precision and over-stemming avoidance. |
| F1 | `2 TP / (2 TP + FN + FP)` | `[0, 1]`; higher is better. | Equal precision/recall emphasis. |
| F2 | `5 TP / (5 TP + 4 FN + FP)` | `[0, 1]`; higher is better. | Gives greater weight to recall and under-stemming avoidance. |
| Jaccard | `TP / (TP + FP + FN)` | `[0, 1]`; higher is better. | Excludes TN. All policies. |
| FowlkesMallows | `sqrt(precision * recall)` | `[0, 1]`; higher is better. | Geometric balance of precision and recall. All policies. |
| MCC | `(TP TN - FP FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))` | `[-1, 1]`; higher is better. Uses all four counts. | Informative under imbalance; undefined for a zero product denominator. All policies with policy-specific interpretation. |
The general F-beta formula is `((1 + betaSquared) * TP) / (((1 + betaSquared) * TP) + (betaSquared * FN) + FP)`.
## Partition-only metrics
These metrics apply only to `PRIMARY_OUTPUT`. Candidate relations are not forced into artificial partitions.
- Adjusted Rand Index is the Rand agreement corrected for agreement expected from the gold/predicted contingency-table marginals. Its usual range is `[-1, 1]`, with `1` indicating identical partitions.
- Homogeneity is `1 - H(gold | predicted) / H(gold)`, in `[0, 1]`; each predicted cluster ideally contains one gold group.
- Completeness is `1 - H(predicted | gold) / H(predicted)`, in `[0, 1]`; each gold group ideally maps to one predicted cluster.
- V-measure is the harmonic mean of homogeneity and completeness, in `[0, 1]`.
- Normalized mutual information uses arithmetic-mean entropy normalization: `MI / ((H(gold) + H(predicted)) / 2)`, in `[0, 1]` under this implementation.
Entropy zero cases follow the evaluator's explicit perfect/undefined conventions. Language tables render inapplicable candidate-policy values as `n/a`.
## Aggregation and ranking
Macro metrics average defined per-language values, giving each language equal weight. Micro metrics sum TP, FP, FN, and TN before calculating a metric. Cross-stemmer aggregate comparisons require the exact common supported-language intersection; unsupported languages are not zero-filled.
Language tables sort by unrounded balanced accuracy, then MCC, F1, over-stemming rate, over-stemming error count, under-stemming rate, stemmer name, and stable policy order. Display rounding never controls rank.
Multiple metrics and Pearson/Spearman correlation datasets are published because metric suitability and correlation remain analytical questions. Strong correlation does not establish equivalence.
## Limitations
Dictionary groups encode the available annotation, not every linguistic distinction. Homographs may occur in different groups, singleton rows contribute no under-stemming pair, and group size affects pair counts. `ANY_CANDIDATE` is optimistic; `ALL_CANDIDATES` measures an overlapping graph; neither is a deterministic global assignment. Results characterize the tested versions, adapters, dictionaries, and preprocessing, not every deployment or domain.

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# Benchmark Methodology
The stemmer comparison suite measures Radixor and Java stemmers on the same language and deterministic Radixor model dictionary-derived data. Published Radixor rows in this refresh use contracted compiled patch tries, where uniform preferred-command subtrees are collapsed into accepting leaves before the trie is frozen for lookup. For each language, the registered default model resource stores the expected root as the first tab-separated field on a line and its surface forms on the same line. Every single-token field on that line can therefore be paired with the same expected root.
Published stemmer comparison results must come only from benchmark classes matching `.*StemmerComparisonBenchmark.*`. Internal `FrequencyTrie*` microbenchmarks are not part of those results.
## Benchmark Passes
There are two distinct benchmark passes:
- Speed benchmarks process only changed dictionary pairs where `token != expectedRoot`. This removes already-root tokens from timing so a stemmer is measured on words that actually require a transformation. If a language has fewer than 5,000 changed pairs, the complete changed-pair sequence is repeated in stable order until the timing corpus has at least 5,000 tokens. Larger changed-pair corpora are not sampled or truncated.
- Quality benchmarks process the complete dictionary for the language. They report exact agreement over all tokens, exact agreement over changed tokens only, and preservation of tokens that are already roots.
Timing corpora are generated once per JMH JVM and kept in memory as shared `{token, expectedRoot}` arrays. Corpus construction, dictionary loading, trie loading, table loading, and analyzer construction are setup work and are not included in measured benchmark methods.
Performance is interpreted as average time per input token:
```text
timePerChangedTokenNs = JMH score ns/op / changedTimingTokenCount
```
This is necessary because Radixor dictionaries have different token counts by language.
## Exact-root quality and interpretation
Runtime and exact-root agreement must be interpreted separately. Light, minimal, possessive, and aggressive rule-based implementations deliberately address different scopes and may achieve lower latency by performing fewer transformations. A throughput advantage does not establish higher linguistic quality, and higher dictionary agreement does not establish lower operational cost.
The [English dictionary coverage benchmark](english-coverage.md) shows this operating curve explicitly: contracted tries reduce lookup cost in uniform regions, while reduced dictionary coverage still lowers changed-form precision.
## Normalization Policy
Radixor is measured over dictionary tokens from its own resources: lower-case with `Locale.ROOT`, diacritics preserved. The corpus is normalized during setup, so the Radixor benchmark path uses `FrequencyTrie.getNormalized(CharSequence)` and does not measure redundant lookup-time lowercasing or diacritic normalization.
Lucene TokenFilter paths include required normalization in the measured pipeline. Examples include lower-case normalization for filters requiring lower-case input, German normalization before German light/minimal stemming, and Persian decimal, Arabic, and Persian normalization before Persian stemming. No ASCII folding is applied to Czech or Polish paths, because those Lucene stemmers are diacritic-aware or dictionary/table-backed for those languages. TokenFilter throughput methods materialize each emitted `CharTermAttribute` as a `String` before passing it to the JMH `Blackhole`, so output consumption is easier to inspect and closer to the direct stemmer methods.
For right-to-left Radixor languages, patch application uses the traversal direction stored in trie metadata. This is required because static backward patch application is not correct for all registered language models.
## Quality Metric
The quality pass reports exact-root agreement against the expected root from the Radixor dictionary line. It writes to the normal JMH report files:
- `build/reports/jmh/jmh-results.csv`
- `build/reports/jmh/jmh-results.txt`
Accuracy is computed from standard JMH secondary rows:
```text
allExactPercent = correctMatches / evaluatedTokens * 100
changedExactPercent = changedCorrectMatches / changedEvaluatedTokens * 100
rootPreservedPercent = rootPreservedMatches / rootEvaluatedTokens * 100
```
`allExactPercent` uses the complete dictionary. `changedExactPercent` uses only tokens where `token != expectedRoot`. `rootPreservedPercent` measures whether a stemmer leaves already-root dictionary entries unchanged.
Morfologik can emit multiple terms for one input token. The quality benchmark uses the first emitted term for exact-root accounting when no ranking weight is exposed. Throughput benchmarks for Morfologik TokenFilter paths consume all emitted terms.
Quality reports use JMH auxiliary counter rows. Exact-root accounting is deterministic for a fixed corpus and stemmer, so repeated measurement samples duplicate the same counters; documentation uses the counter ratios and does not interpret quality benchmark timing scores.
Pairwise over-stemming, under-stemming, candidate-aware policies, balanced accuracy, and partition comparison are a separate analytical evaluation. See [Linguistic Quality Methodology](linguistic-quality.md); exact-root accuracy must not be interpreted as the complement of pairwise under-stemming.
Default rows use `Language.defaultModelId()`. Optional variants require a separate model field; `pl-pl-unimorph` and `pl-pl-polimorf` must never share an ambiguous Polish label. The benchmark runtime receives each resource exactly once from its individual model JAR through direct JMH runtime dependencies. See [Model Selection and Loading](../../model-selection-and-loading.md).

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# Reproducibility and Raw Data
## Published quality snapshot
- Machine-readable CSV: [stemming-quality.csv](../data/stemming-quality.csv)
- SHA-256 record: [stemming-quality.sha256](../data/stemming-quality.sha256)
- SHA-256: `5a93a6ab60e46489737cd649eb1ac48182114b9038f7f20195ab9d1c1fc0dd28`
- Complete scenarios: 308
- Authoritative language universe: 20 languages
- Language-page scenarios: 302 across 19 existing benchmark pages
The six remaining scenarios are the three Radixor policies in two modes for `HE_IL`. Hebrew is present in the complete result snapshot but has no existing language benchmark page.
The CSV contains raw TP, FP, FN, and TN counts; raw over/under numerators and denominators; candidate statistics; relation metrics; and partition-only metrics. Documentation is regenerated from this file rather than manually transcribed.
## Commands
```bash
./gradlew stemmingQuality
./gradlew publishStemmingQualityDocumentation
./gradlew verifyStemmingQualityDocumentation
./gradlew test
./gradlew prepareMkDocsSource
mkdocs build --strict --config-file build/mkdocs/mkdocs.yml
```
`stemmingQuality` performs the expensive complete evaluation and is intentionally not attached to `test` or `check`. It prepares JMH third-party dependencies automatically and writes:
- `build/reports/stemming-quality/stemming-quality.csv`
- `build/reports/stemming-quality/stemming-quality.md`
- `build/reports/stemming-quality/metric-correlations-pearson.csv`
- `build/reports/stemming-quality/metric-correlations-spearman.csv`
Audit mode is enabled with `-PstemmingQualityAudit=true`. Language, stemmer, dictionary-mode, output-policy, and ranking filters are documented on the central [stemming-quality page](../../stemming-quality.md). Filtered reports use separate filenames and cannot be accepted as publication sources.
`publishStemmingQualityDocumentation` validates the complete build CSV, copies a versioned documentation snapshot, and replaces only marked generated sections. `verifyStemmingQualityDocumentation` re-renders from the checked-in snapshot and fails on changed values, ordering, missing pages, duplicate keys, arithmetic inconsistencies, policy violations, or stale sections.
The model catalog and rendered site are build outputs under `build/`. They are generated for publication and are never maintained in Git.
For new measurements, record language, stable model ID, model artifact version, descriptor checksum, source dictionary identity/version, core revision, and benchmark configuration. JMH resolves the required default models and optional PoliMorf directly from their individual model JARs; these benchmark-only dependencies are not transitive to ordinary users.
Current model descriptors also record the official repository, dataset, license, attribution,
verification date, transformations, and source-revision status. Exact historical revisions were
not recorded for the legacy UniMorph imports; that limitation is disclosed with
`not-recorded-in-legacy-import` rather than reconstructed. Future imports must record the exact
upstream revision and source-archive checksum. This reproducibility limitation does not replace or
weaken the packaged license and attribution requirements.
Each UniMorph-derived model artifact carries its own notice with the canonical CC BY-SA 3.0 URI,
upstream attribution, transformations, ShareAlike statement, and Leo Galambos contribution notice.
The full CC legal text is not duplicated or presented as a root-project license. PoliMorf retains
its separately packaged BSD-2-Clause license.
For a future full PoliMorf measurement, also record the startup heap separately from benchmark parameters. Complete runtime construction is currently verified with a dedicated 6 GiB maximum heap; this limit is neither a retained-trie measurement nor a setting applied to ordinary JMH runs.
The Pages workflow publishes that staged documentation together with Javadoc, JUnit, PMD, JaCoCo, PIT, representative JMH, SBOM, optional dependency-check output, badge metadata, and retained build history. Its filesystem merge explicitly preserves the `builds/` tree in the separate `gh-pages` publication branch, so documentation regeneration cannot erase durable report URLs.
## Performance benchmark reproduction
The JMH comparison command family is:
```bash
./gradlew jmh -Pjmh.includes='.*StemmerComparisonBenchmark.*' --no-daemon
```
The exact JMH configuration, hardware, operating system, and JDK captured for the published performance tables are listed in [Environment and reports](environment.md). Quality and performance reports are separate datasets and are not combined into an undocumented scalar.
## Recorded and unavailable provenance
The performance documentation records its 2026-07-06 environment, JDK 25.0.3, operating system, and hardware. The quality CSV records the evaluated identifiers and counts but does not embed the Radixor Git revision, generation date, JDK, operating system, model ID, dictionary content hash, or immutable upstream revisions for every downloaded source. These fields are explicitly unavailable for this historical snapshot and are not reconstructed from filesystem timestamps. In particular, the snapshot predates the optional PoliMorf integration and must not be relabeled as `pl-pl-polimorf`.
Dependency versions that are reproducible from repository configuration include Apache Lucene 10.5.0, Morfologik 2.1.9, the Ukrainian dictionary artifact 4.9.1, and JMH 1.37. Other upstream branches or downloaded dictionary revisions should be pinned and embedded in a future result schema.
## Correlation and audit data
Pearson and Spearman files are generated from unrounded metric values in cohorts separated by dictionary mode and output policy. A missing coefficient means too few observations, undefined input, or zero variance. Correlation is descriptive and does not demonstrate that two metrics are scientifically interchangeable.
Audit reports preserve original multilingual forms and identify high-contributing dictionary groups. They are build artifacts rather than checked-in publication data because of their size. No documentation value is manually altered after generation.
## JMH badge compatibility
The quality documentation generator does not invoke JMH, change JMH result formats, or modify badge tooling. Existing JMH result paths and historical badge-compatible inputs remain independent. The repository currently publishes coverage and mutation badge metadata and retains JMH TXT/CSV artifacts as documented in [Environment and reports](environment.md).
See [Model Selection and Loading](../../model-selection-and-loading.md), [Stemmer Models](../../stemmer-models.md), and the generated [model catalog](../../stemmer-model-catalog.md) for current model identities.

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# Tested Stemmer Inventory
The JMH adapter registry is authoritative for evaluated implementations and language mappings. Names below describe the implementation actually invoked, not an abstract algorithm in every possible implementation. Unsupported language combinations are omitted rather than scored as failures.
| Family or implementation | Upstream / attribution | Tested version or revision | Evaluated scope | Output capability and adapter behaviour | Interpretation notes |
| --- | --- | --- | --- | --- | --- |
| Radixor | Egothor / Radixor project | Current repository revision; exact revision was not embedded in the quality CSV | All 20 reconciled default model languages; 19 have benchmark pages | Deterministic preferred patch via `get`; ranked distinct alternatives via `getAll`; primary is always included | Model-dictionary-derived compiled patch trie. Default rows use each language's stable default model ID. |
| Apache Lucene language stem filters | Apache Lucene project | 10.5.0 | Adapter-declared language-specific subsets | TokenFilter lifecycle and language normalization match JMH; normally single-output | Light, minimal, possessive, and language stem filters deliberately implement different scopes. Narrow scope is not a defect. |
| Apache Lucene SnowballFilter | Apache Lucene project using Snowball algorithms | Lucene 10.5.0 | Snowball-supported subset of Radixor languages | Single primary token emitted through the Lucene TokenFilter path | Includes TokenStream overhead and required normalization. |
| Official Snowball Java | Snowball project | Repository preparation downloads the configured upstream Java distribution; an immutable revision was not recorded in the quality CSV | Same-language adapter subset | Direct generated Java API; single output | Rule-based suffix algorithms provide broad baselines rather than dictionary-root guarantees. |
| Lucene Stempel | Apache Lucene / Polish stemming tables | Lucene 10.5.0 | Polish | Direct and TokenFilter paths where registered; single primary output | Table-driven Polish implementation. |
| Morfologik | Morfologik project; Lucene integration by Apache Lucene | Morfologik 2.1.9, Lucene integration 10.5.0; Ukrainian dictionary artifact 4.9.1 | Registered Polish and Ukrainian paths | Deterministic first lemma for primary comparison; all distinct lemma strings for candidate policies | Several analyses may share a lemma and are deduplicated by exact string equality. |
| Hunspell via Lucene | Hunspell dictionaries from the `wooorm/dictionaries` repository; adapter by Apache Lucene | Lucene 10.5.0; dictionary repository revision was not recorded | Configured German, English, Spanish, French, Dutch, Polish, and Ukrainian dictionaries | First emitted stem is primary; all distinct stems at the token position are candidates | Dictionary content and affix rules differ by language. |
| CISTEM | Leonie Weissweiler, CISTEM project | Upstream `master` source path used by preparation; immutable commit not recorded | German | Single output | German stemming algorithm; benchmark-only implementation and gold-standard preparation remain under JMH infrastructure. |
| OpenNLP Porter | Apache OpenNLP project | Version resolved by `gradle/opennlp-benchmarks.gradle` and `gradle.lockfile` | English | Direct single output | Porter-family English baseline. |
| Lucene Porter source copy | Apache Lucene project | 10.5.0 source artifact | English | Package-isolated benchmark-only generated source; single output | Generated into the JMH build tree, never production code. |
| Paice/Husk Lancaster | Upstream Java implementation from `Hopper262/paice-husk-stemmer` | Configured upstream branch/revision in `gradle/paicehusk-benchmarks.gradle`; immutable commit not recorded | English | Direct single output | Aggressive rule-based English baseline; benchmark-only generated source. |
## Preprocessing and lifecycle
The quality evaluator calls the same adapter matrix used by JMH. Each language mapping is explicit. Retained dictionary forms are not evaluation-lowercased or normalized. Where an implementation requires preprocessing, such as Lucene German or Persian normalization, that operation is part of its documented adapter path. Stateful TokenFilters are reset through the same sequential lifecycle used by the benchmark and are not invoked concurrently.
Candidate sets are non-null, non-empty, contain the deterministic primary output, contain no null strings, and are deduplicated using exact Java string equality. Gold-standard group identity never selects, removes, or ranks a candidate.
## Coverage fairness
Radixor coverage is derived from registered default descriptors reconciled with language enumeration. Third-party coverage is the intersection of that universe with actual adapter support. Absence therefore means “not supported or not configured for this language,” not “zero quality.” Optional `pl-pl-polimorf` is a separate model row and does not replace default `pl-pl-unimorph`. Consult each language page for the exact evaluated rows.
Project authors and organizations are named only where repository configuration or source notices establish attribution. No broader authorship or license claim is inferred when metadata was not captured.
The JMH runtime configuration directly includes optional models needed for controlled comparisons; ordinary users do not receive these benchmark-only dependencies transitively. Historical rows retain their original model inputs. See [Model Selection and Loading](../../model-selection-and-loading.md).

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# Built-in Languages
# Built-in Languages and Default Models
Radixor ships with a curated set of bundled stemmer dictionaries that can be loaded directly from the library distribution. These resources are intended to provide an immediately usable baseline for evaluation, prototyping, integration, and general-purpose stemming workloads, while still fitting naturally into workflows where the bundled baseline is later refined, extended, or replaced with custom lexical data.
“Supported language” means that Radixor defines a language enum value and publishes a corresponding default model artifact. It does not mean that a dictionary is embedded in the core JAR. Applications add model artifacts explicitly or use the optional standard pack.
## Overview
The language enum carries language identity, writing direction, a legacy resource-directory name, and the stable default model ID. A model descriptor carries the independently versioned model identity and resource. See [Model Selection and Loading](model-selection-and-loading.md) for the API and the generated [model catalog](stemmer-model-catalog.md) for versions, provenance, checksums, and sizes.
Bundled dictionaries are exposed through:
## Defaults and variants
```java
org.egothor.stemmer.StemmerPatchTrieLoader.Language
```
| Language | Enum | Default model ID | Default artifact | Optional variants |
|---|---|---|---|---|
| Czech | `CS_CZ` | `cs-cz-default` | `org.egothor:radixor-model-cs-cz-default` | — |
| Danish | `DA_DK` | `da-dk-default` | `org.egothor:radixor-model-da-dk-default` | — |
| German | `DE_DE` | `de-de-default` | `org.egothor:radixor-model-de-de-default` | — |
| Spanish | `ES_ES` | `es-es-default` | `org.egothor:radixor-model-es-es-default` | — |
| Persian | `FA_IR` | `fa-ir-default` | `org.egothor:radixor-model-fa-ir-default` | — |
| Finnish | `FI_FI` | `fi-fi-default` | `org.egothor:radixor-model-fi-fi-default` | — |
| French | `FR_FR` | `fr-fr-default` | `org.egothor:radixor-model-fr-fr-default` | — |
| Hebrew | `HE_IL` | `he-il-default` | `org.egothor:radixor-model-he-il-default` | — |
| Hungarian | `HU_HU` | `hu-hu-default` | `org.egothor:radixor-model-hu-hu-default` | — |
| Italian | `IT_IT` | `it-it-default` | `org.egothor:radixor-model-it-it-default` | — |
| Norwegian Bokmål | `NB_NO` | `nb-no-default` | `org.egothor:radixor-model-nb-no-default` | — |
| Dutch | `NL_NL` | `nl-nl-default` | `org.egothor:radixor-model-nl-nl-default` | — |
| Norwegian Nynorsk | `NN_NO` | `nn-no-default` | `org.egothor:radixor-model-nn-no-default` | — |
| Polish | `PL_PL` | `pl-pl-unimorph` | `org.egothor:radixor-model-pl-pl-unimorph` | `pl-pl-polimorf` / `org.egothor:radixor-model-pl-pl-polimorf` |
| Portuguese | `PT_PT` | `pt-pt-default` | `org.egothor:radixor-model-pt-pt-default` | — |
| Russian | `RU_RU` | `ru-ru-default` | `org.egothor:radixor-model-ru-ru-default` | — |
| Swedish | `SV_SE` | `sv-se-default` | `org.egothor:radixor-model-sv-se-default` | — |
| Ukrainian | `UK_UA` | `uk-ua-default` | `org.egothor:radixor-model-uk-ua-default` | — |
| English | `US_UK` | `us-uk-default` | `org.egothor:radixor-model-us-uk-default` | — |
| Yiddish | `YI` | `yi-default` | `org.egothor:radixor-model-yi-default` | — |
Each bundled dictionary is packaged with the library as a compressed UTF-8 text resource. When loaded, the resource is parsed by `StemmerDictionaryParser`, transformed into patch-command mappings, and compiled into a read-only `FrequencyTrie<String>` by `StemmerPatchTrieLoader`.
The maintained table deliberately avoids duplicating mutable provenance and checksum fields. Those values come from module metadata and are generated into the model catalog.
The bundled language definition also carries a language-level right-to-left flag. That flag is used by the loader to derive the `WordTraversalDirection` used for both trie-key construction and patch-command generation. In practice, left-to-right bundled languages use historical backward Egothor traversal, while right-to-left bundled languages use forward traversal over the stored form.
## The Polish dual-model case
## Supported bundled languages
`PL_PL` represents Polish. It is not an alias for either source dictionary.
The following bundled language identifiers are currently available:
- `loadCompiled(Language.PL_PL, ...)` resolves `pl-pl-unimorph`.
- `registry.require("pl-pl-polimorf")` resolves the optional PoliMorf model.
- `StemmerPatchTrieLoader.loadCompiled("pl-pl-polimorf", true, reductionMode)` constructs its compiled trie explicitly; complete construction is verified with a dedicated 6 GiB test heap.
- Both artifacts may be present and loaded independently.
- Adding PoliMorf does not change the language default.
- Radixor does not merge their dictionaries or outputs automatically.
| Language | Enum constant | Writing direction | Notes |
|---|---|---:|---|
| Czech | `CS_CZ` | LTR | Bundled general-purpose dictionary |
| Danish | `DA_DK` | LTR | Bundled general-purpose dictionary |
| German | `DE_DE` | LTR | Bundled general-purpose dictionary |
| Spanish | `ES_ES` | LTR | Bundled general-purpose dictionary |
| Persian | `FA_IR` | RTL | Bundled dictionary uses forward traversal over the stored form |
| Finnish | `FI_FI` | LTR | Bundled general-purpose dictionary |
| French | `FR_FR` | LTR | Bundled general-purpose dictionary |
| Hebrew | `HE_IL` | RTL | Bundled dictionary uses forward traversal over the stored form |
| Hungarian | `HU_HU` | LTR | Bundled general-purpose dictionary |
| Italian | `IT_IT` | LTR | Bundled general-purpose dictionary |
| Norwegian Bokmål | `NB_NO` | LTR | Bundled general-purpose dictionary |
| Dutch | `NL_NL` | LTR | Bundled general-purpose dictionary |
| Norwegian Nynorsk | `NN_NO` | LTR | Bundled general-purpose dictionary |
| Polish | `PL_PL` | LTR | Bundled general-purpose dictionary |
| Portuguese | `PT_PT` | LTR | Bundled general-purpose dictionary |
| Russian | `RU_RU` | LTR | Bundled general-purpose dictionary |
| Swedish | `SV_SE` | LTR | Bundled general-purpose dictionary |
| Ukrainian | `UK_UA` | LTR | Bundled general-purpose dictionary |
| English | `US_UK` | LTR | Bundled general-purpose dictionary |
| Yiddish | `YI` | RTL | Bundled dictionary uses forward traversal over the stored form |
UniMorph and PoliMorf have different lexical sources and provenance. Applications should compare outputs with application-specific regression tests before changing an explicit model choice.
## Basic usage
## Dependency patterns
Load a bundled dictionary like this:
Minimal English:
```java
import java.io.IOException;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class BuiltInExample {
private BuiltInExample() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
System.out.println(trie.traversalDirection());
}
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-us-uk-default:1.0.0'
}
```
This call loads the bundled dictionary resource for the selected language, parses its lexical entries, derives patch-command mappings, and compiles the result into a read-only trie.
All documented defaults:
## Example: stemming with a bundled dictionary
```java
import java.io.IOException;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.PatchCommandEncoder;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class EnglishExample {
private EnglishExample() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final String word = "running";
final String patch = trie.get(word);
final String stem = PatchCommandEncoder.apply(word, patch, trie.traversalDirection());
System.out.println(word + " -> " + stem);
}
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-models-standard:<catalog-version>'
}
```
Passing `trie.traversalDirection()` to `PatchCommandEncoder.apply(...)` is the correct general contract. It ensures that the patch is applied using the same logical traversal model that was used when the trie and its patch commands were produced.
The standard pack is metadata-only and excludes optional PoliMorf.
## Traversal behavior and right-to-left languages
Every individual model artifact carries its own provenance and licensing material. UniMorph
models carry different model-specific CC BY-SA 3.0 notices because their official language
repositories identify different lexical sources and contributors. Each notice preserves upstream
attribution and records the Radixor transformations and Leo Galambos contribution statement.
Legacy imports disclose when an exact historical revision was not recorded; this is a
reproducibility limitation, not a claim that the source or license is unknown.
Bundled dictionaries are not all processed identically.
For traditional left-to-right suffix-oriented resources, Radixor preserves historical Egothor behavior and traverses logical word characters backward. That means trie paths are constructed from the logical end of the stored word toward its beginning, and patch commands are interpreted with the same backward traversal model.
For bundled right-to-left languages such as Persian, Hebrew, and Yiddish, Radixor uses forward traversal over the stored form. In those cases:
- trie keys are traversed from the logical beginning of the stored form,
- patch commands are generated in that same forward direction,
- patch application must use `WordTraversalDirection.FORWARD`, which is naturally obtained from `trie.traversalDirection()`.
This design keeps the traversal policy explicit and consistent across dictionary loading, trie lookup, binary persistence, builder reconstruction, and patch application.
## Reduction behavior
Bundled dictionaries can be compiled using any supported `ReductionMode`. The reduction configuration controls how semantically equivalent subtrees are merged during trie compilation, while preserving the contract of the selected mode.
Typical entry points are:
- `StemmerPatchTrieLoader.load(language, storeOriginal, reductionMode)`
- `StemmerPatchTrieLoader.load(language, storeOriginal, reductionSettings)`
For most users, `ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS` is the most conservative general-purpose choice because it preserves ranked `getAll(...)` behavior.
## Intended role of bundled dictionaries
Bundled dictionaries should be understood as practical default resources.
They are a good fit when:
- a supported language is already available,
- immediate usability matters,
- a reasonable baseline is sufficient,
- the goal is evaluation, prototyping, or straightforward integration.
They are also well suited to staged refinement workflows in which a bundled base is loaded first, then extended with domain-specific vocabulary, and finally persisted as a custom binary artifact.
## Character representation
Bundled dictionaries are ordinary UTF-8 lexical resources. The parser reads them as text, the trie stores standard Java strings, and the patch-command model operates on general character sequences.
This is important for two reasons:
1. the built-in resources are not limited to ASCII-only processing,
2. the traversal model is orthogonal to character encoding and script choice.
In other words, right-to-left handling in the loader is about logical traversal strategy, not about introducing a separate character model.
## When to prefer custom dictionaries
A custom dictionary is usually the better choice when:
- domain-specific vocabulary materially affects stemming quality,
- lexical coverage must be controlled more precisely,
- a stronger lexical resource is available than the bundled baseline,
- operational requirements demand an explicitly curated, versioned artifact.
Typical examples include:
- technical terminology,
- biomedical language,
- legal or financial vocabulary,
- organization-specific product and process names,
- dictionaries maintained with project-specific validation rules.
## Production recommendation
For production systems, the most robust workflow is usually:
1. start from a bundled dictionary when it is suitable,
2. extend it with domain-specific forms if needed,
3. rebuild it into a binary artifact,
4. deploy that compiled binary artifact,
5. load it at runtime through `loadBinary(...)`.
This avoids repeated startup parsing and makes the deployed stemming behavior explicit, reproducible, and versionable.
## Example refinement workflow
## Loading a language default
```java
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.FrequencyTrieBuilders;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.ReductionSettings;
import org.egothor.stemmer.StemmerPatchTrieBinaryIO;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class BundledRefinementExample {
private BundledRefinementExample() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> base = StemmerPatchTrieLoader.load(
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final FrequencyTrie.Builder<String> builder = FrequencyTrieBuilders.copyOf(
base,
String[]::new,
ReductionSettings.withDefaults(
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS));
builder.put("microservices", "Na");
final FrequencyTrie<String> compiled = builder.build();
StemmerPatchTrieBinaryIO.write(compiled, Path.of("english-custom.radixor.gz"));
}
}
```
The reconstructed builder preserves the traversal direction of the source trie, so refinements remain semantically aligned with the original bundled dictionary.
The call discovers the default descriptor from the runtime classpath, verifies its compressed resource, parses the GZip UTF-8 dictionary, and constructs a read-only trie. A missing default throws `StemmerModelNotFoundException`; there is no arbitrary fallback.
## Extending language support
## Writing direction
The built-in set is intentionally a practical baseline rather than a closed catalog. Additional languages, stronger lexical coverage, and improved dictionaries for currently supported languages are all natural extension paths.
Persian, Hebrew, and Yiddish declare right-to-left language metadata and use forward traversal over stored forms. Other defaults use historical backward Egothor traversal. This setting must remain aligned across dictionary parsing, trie lookup, patch generation, persistence, and application. Model identity remains separate from writing direction.
What matters most is not only the number of entries, but the quality, consistency, maintainability, and operational usefulness of the lexical resource being added.
## Custom and persisted alternatives
## Related API surface
Registered model artifacts are a convenient reproducible baseline. Applications may instead load caller-owned textual dictionaries or persist compiled `.radixor.gz` tries. Those paths are distinct from model artifact discovery:
The following types are typically involved when working with bundled dictionaries:
- a model `stemmer.gz` is a compressed textual dictionary plus descriptor/index metadata;
- a `.radixor.gz` created by the binary writer is a persisted compiled trie;
- a source dictionary is upstream input, not automatically a valid model artifact.
- `StemmerPatchTrieLoader`
- `StemmerPatchTrieLoader.Language`
- `FrequencyTrie`
- `PatchCommandEncoder`
- `WordTraversalDirection`
- `ReductionMode`
- `ReductionSettings`
- `StemmerPatchTrieBinaryIO`
- `FrequencyTrieBuilders`
See [Dictionary Format](dictionary-format.md), [CLI Compilation](cli-compilation.md), and [Stemmer Models](stemmer-models.md).
## Next steps
## Benchmark interpretation
- [Quick start](quick-start.md)
- [Dictionary format](dictionary-format.md)
- [CLI compilation](cli-compilation.md)
- [Programmatic usage](programmatic-usage.md)
## Summary
Radixors built-in language support provides immediate usability, a professionally defined baseline API, and a practical starting point for custom refinement. The bundled set now includes both left-to-right and right-to-left languages, and the library models that distinction explicitly through `WordTraversalDirection` so that trie construction, lookup, and patch application remain consistent.
Benchmark rows must identify the Radixor model ID used. Default rows use the default IDs above. Optional Polish PoliMorf comparisons must be labeled `pl-pl-polimorf`; they are not interchangeable with the historical default Polish row. Continue with [Benchmarking](benchmarking.md) and [Reproducibility](benchmarks/reference/reproducibility.md).

View File

@@ -2,6 +2,8 @@
Radixor provides a command-line compiler for turning line-oriented dictionary files into compact binary stemmer artifacts.
The CLI output is not a model JAR. A model artifact contains a compressed textual dictionary, descriptor, index, checksum, and license so the runtime registry can discover and compile it. The CLI instead emits an already compiled binary trie for direct `loadBinaryCompiled(...)` use. Choose the model-module workflow when independently published classpath discovery is required; choose the CLI when the application owns a compiled binary asset.
This is the preferred preparation workflow when stemming should run against an already compiled artifact rather than against raw dictionary input. The CLI reads the dictionary, derives patch commands, builds a mutable trie, applies the selected subtree reduction strategy, and writes the final compiled trie in the project binary format under GZip compression. The result is a deployment-ready `.radixor.gz` file that can be loaded directly by application code.
## What the CLI does
@@ -17,6 +19,10 @@ The `Compile` tool performs the following steps:
This workflow is intentionally aligned with the same dictionary semantics used elsewhere in the library. Remarks introduced by `#` or `//` are supported through the shared dictionary parser.
## Create a registered custom model instead
To publish or deploy a custom dictionary through `StemmerModelRegistry`, do not merely rename CLI output to `stemmer.gz`. Create `models/<model-id>`, preserve the textual dictionary as a GZip module input, provide source metadata and a license, apply `org.egothor.radixor.model`, and run the model validation tasks. The resulting JAR has an index, descriptor, namespaced textual dictionary, checksum, and license. Detailed packaging is documented in [Stemmer Models](stemmer-models.md); selection is documented in [Model Selection and Loading](model-selection-and-loading.md).
## Basic usage
```bash
@@ -171,6 +177,11 @@ The short form `-h` is also supported.
Reduction mode is not only a storage decision. It also influences what semantics are preserved when the mutable trie is compiled into its canonical read-only form.
Before the selected public reduction mode is applied, compilation performs uniform-subtree
contraction. If all reachable entries below a subtree select the same preferred patch command, the
compiler stores that subtree as an accepting leaf and removes the deeper branches. This reduces
runtime lookup depth without changing the preferred result returned by the standard stemming path.
### Ranked `getAll()` equivalence
`MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS` merges subtrees whose `getAll()` results remain equivalent for every reachable key suffix and whose local result ordering is the same.
@@ -203,9 +214,9 @@ The CLI is best used as a preparation step during packaging, deployment, or cont
### Treat compiled files as versioned assets
A `.radixor.gz` file should be handled as a versioned output artifact. It represents a specific dictionary state, a specific reduction mode, and, where relevant, specific dominant-result thresholds.
A `.radixor.gz` file should be handled as a versioned output artifact. It represents a specific dictionary state, a specific reduction mode, whether uniform-subtree contraction was used, and, where relevant, specific dominant-result thresholds.
Compiled tries also persist a human-readable metadata block (`key=value` lines) that includes format version, traversal direction, RTL indicator, reduction mode, dominant thresholds, diacritic-processing mode, and case-processing mode. After decompression, you can inspect this block directly to identify what dictionary/trie configuration the artifact contains. The current CLI uses `DiacriticProcessingMode.AS_IS`; custom diacritic stripping is available through the programmatic builder and loader APIs rather than through a CLI flag.
Compiled tries also persist a human-readable metadata block (`key=value` lines) that includes format version, traversal direction, RTL indicator, reduction mode, contraction flag, dominant thresholds, diacritic-processing mode, and case-processing mode. After decompression, you can inspect this block directly to identify what dictionary/trie configuration the artifact contains. The current CLI uses `DiacriticProcessingMode.AS_IS`; custom diacritic stripping is available through the programmatic builder and loader APIs rather than through a CLI flag.
### Choose reduction mode deliberately
@@ -215,6 +226,8 @@ The ranked `getAll()` mode is the safest default. The unordered and dominant mod
Compilation is usually a one-time step and is generally fast. The more important operational consideration is memory usage during preparation, because the dictionary-derived mutable structure exists before reduction compacts it into the final read-only trie. This is especially relevant for very large source dictionaries.
The complete PoliMorf model is the current exceptional case: registered-model verification uses `runtimeModelIntegrationTest` with a 6 GiB maximum heap, configurable through `-PradixorLargeModelMaxHeap=<size>`. This setting applies only to that isolated test process, not the Gradle daemon or ordinary tests.
## Example workflow
### 1. Prepare a dictionary
@@ -237,11 +250,12 @@ java org.egothor.stemmer.Compile \
### 3. Load it in an application
```java
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
final FrequencyTrie<String> trie =
StemmerPatchTrieLoader.loadBinary("english.radixor.gz");
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadBinaryCompiled("english.radixor.gz");
```
## Exit codes and error handling
@@ -276,3 +290,5 @@ The CLI and the programmatic API implement the same conceptual preparation step.
- [Quick start](quick-start.md)
- [Programmatic usage](programmatic-usage.md)
- [Architecture and reduction](architecture-and-reduction.md)
!!! note "Radixor 4 model artifacts"
Language dictionaries are independently versioned runtime model artifacts, not resources embedded in `radixor`. Language-based APIs resolve deterministic defaults through `StemmerModelRegistry`; see [Stemmer Models](stemmer-models.md).

View File

@@ -37,7 +37,7 @@ This API is expected to remain supportable across future versions. The preferred
Examples of likely additive evolution include:
- additional bundled language resources,
- additional independently versioned language models,
- fuller support for diacritics or native-script language resources,
- expanded documentation and operational tooling,
- new convenience methods that do not break existing code.
@@ -81,7 +81,13 @@ Compiled `FrequencyTrie` instances are immutable and thread-safe for concurrent
### Stable patch application behavior
`PatchCommandEncoder.apply(...)` remains the compatibility API for string-returning patch application. Buffer-oriented `applyTo(...)` overloads are additive APIs for caller-owned output storage. They do not retain output arrays, report insufficient capacity with `APPLY_INSUFFICIENT_CAPACITY`, and preserve the existing malformed-patch compatibility behavior where `apply(...)` preserves the source.
Serialized patch-command strings remain the stable stored representation used by textual dictionaries and binary artifacts. Runtime stemming should use `CompiledPatchCommand` values produced by `StemmerPatchTrieLoader.loadCompiled(...)`, `StemmerPatchTrieLoader.loadBinaryCompiled(...)`, or `PatchCommandEncoder.compile(...)`.
Language-default, descriptor, and stable model-ID `loadCompiled` entry points share the same compiled-value conversion. Explicit model IDs never fall back to a language default. Model loading is not cached, and construction-memory requirements are model-dependent; the unusually large PoliMorf input is verified separately with a 6 GiB maximum heap.
The historical `PatchCommandEncoder.apply(...)` and String-based `applyTo(...)` overloads remain compatibility APIs during the 2.x transition, but they are deprecated because they reparse the patch-command string on each application. See [Migration and Backward Compatibility](migration-and-backward-compatibility.md) for old and new code examples.
Compiled buffer-oriented `CompiledPatchCommand.applyTo(...)` overloads use caller-owned output storage. They do not retain output arrays and report insufficient capacity with `CompiledPatchCommand.APPLY_INSUFFICIENT_CAPACITY`.
### Stable reduction-mode intent
@@ -89,6 +95,11 @@ Each public `ReductionMode` constant carries a semantic contract that should rem
In other words, the implementation may evolve, but the intended meaning of modes such as ranked `getAll()` equivalence, unordered `getAll()` equivalence, and dominant `get()` equivalence should not drift casually.
Internal pre-reduction optimizations may still change the physical compiled trie shape when they
preserve the documented lookup contract. Uniform-subtree contraction is one such optimization: it
can replace a subtree with an accepting leaf when all reachable entries choose the same preferred
patch command.
### Stable binary artifact purpose
Compiled `.radixor.gz` artifacts are a first-class project output. Loading and persisting compiled stemmer artifacts is part of the intended usage model, not an incidental implementation side effect.
@@ -101,7 +112,7 @@ The following kinds of change are generally compatible with the projects dire
- improved internal data structures,
- changes inside `org.egothor.stemmer.trie`,
- expanded bundled dictionaries,
- expanded model dictionaries,
- additional supported languages,
- improved native-script handling,
- better benchmarks, tests, and reports,
@@ -113,11 +124,11 @@ The project should be able to improve substantially while keeping the main user-
Some areas should be treated as stable in intent but still approached carefully when changed.
### Bundled dictionary contents
### Independently versioned model contents
Bundled resources are versioned project data, not immutable language standards. Their contents may improve over time.
Model resources are independently versioned project data, not immutable language standards. Their contents may improve over time.
That means stemming outcomes can legitimately change when bundled dictionaries are refined or expanded. Such changes are compatible with the projects direction, but they should still be understood as behavior changes at the lexical-resource level.
That means stemming outcomes can legitimately change when a model artifact is updated. Such changes are separate from core compatibility and should be reviewed as lexical-resource behavior changes.
### Binary format evolution
@@ -150,7 +161,7 @@ Users should avoid depending on:
- internal trie package details,
- undocumented internal classes or intermediate representations,
- incidental internal ordering outside documented lookup semantics,
- assumptions that bundled dictionary contents will never evolve,
- assumptions that a model's dictionary contents will never evolve across model versions,
- assumptions that internal binary-format details are frozen forever.
If a behavior is important to your integration, it should ideally be documented at the public API or project-documentation level rather than inferred from internal implementation details.

View File

@@ -2,7 +2,7 @@
High-quality dictionaries are one of the most valuable ways to improve **Radixor**.
The project already includes practical bundled dictionaries for common use, but the long-term quality and language reach of the stemmer depend heavily on the quality of its lexical resources. Contributions are therefore welcome not only in the form of code changes, but also in the form of well-prepared dictionary data for existing or additional languages.
The project already publishes practical model dictionaries for common use, but long-term quality and language reach depend heavily on lexical-resource quality. Contributions may provide well-prepared model inputs for existing or additional languages.
This document explains what makes a dictionary contribution useful, how to structure it, and how to prepare it so that it integrates cleanly with the project.
@@ -52,7 +52,7 @@ For full format details, see [Dictionary format](dictionary-format.md).
The most useful dictionary contributions generally fall into one of four categories.
### 1. Stronger dictionaries for already bundled languages
### 1. Stronger models for already supported languages
Improving lexical quality for already supported languages is often more valuable than merely expanding the language list. Better coverage, cleaner canonicalization, and improved consistency directly improve practical stemming outcomes.
@@ -68,7 +68,7 @@ That convention belongs to the supplied dictionaries, not to the underlying algo
### 4. Domain-quality refinements
Some contributions may be more appropriate as curated domain extensions than as replacements for a general-purpose bundled dictionary. These are still useful when they are clearly scoped and operationally coherent.
Some contributions may be more appropriate as curated domain extensions than as replacements for a general-purpose default model. These are still useful when clearly scoped and operationally coherent.
## Normalization guidance
@@ -139,6 +139,14 @@ A dictionary should read like a curated lexical resource, not like an unfiltered
## Practical preparation workflow
Before conversion, record the official source project and repository, exact revision or release,
source-archive checksum, retrieval date, dataset license and URI, supplied attribution, and any
required upstream notice. Add a model-specific notice describing every material transformation and
the license applied to the derived data, including its canonical URI. Record any protectable
Radixor-specific contribution without claiming ownership over the upstream data. A legacy model
may disclose that its historical revision was not recorded; new imports must record an exact
revision and source-archive checksum rather than using that sentinel.
A disciplined dictionary contribution should typically follow this path:
1. prepare or normalize the lexical source,
@@ -183,7 +191,7 @@ This note does not need to be long. It simply needs to make the resource intelli
## Bundled-resource expectations
Not every useful dictionary must automatically become a bundled language resource.
Not every useful dictionary must automatically become a published default model.
To be suitable for bundling, a dictionary should generally be:

View File

@@ -2,6 +2,27 @@
Radixor uses a simple line-oriented dictionary format designed for practical stemming workflows. The textual source format is tab-separated values, meaning that columns are separated by the tab character.
## Source text, model resource, and compiled trie
Three artifacts must not be confused:
| Artifact | Representation | Consumer |
|---|---|---|
| Source textual dictionary | Plain UTF-8 tab-separated rows | Authors, parser, CLI, or model preparation |
| Registered model resource | The same Radixor dictionary bytes under GZip, accompanied by index, descriptor, checksum, and license | `StemmerModelRegistry` and `StemmerPatchTrieLoader` |
| Persisted compiled trie | GZip-compressed Radixor binary format, commonly `.radixor.gz` | `loadBinaryCompiled(...)` |
The model file named `stemmer.gz` is not Java serialization and is not a pre-instantiated or persisted trie. It is compressed textual dictionary input parsed when the model is loaded.
Consequently, compressed size is not a construction-memory estimate. The PoliMorf resource is 12,624,997 bytes compressed and 68,093,680 bytes decompressed, while full parsing, trie construction, reduction, and patch compilation require a dedicated verification JVM with a 6 GiB maximum heap.
Comment headers in maintained model inputs summarize provenance but do not replace packaged legal
material. Each UniMorph-derived artifact includes a language-specific notice describing its
official repository, lexical source, upstream attribution, CC BY-SA 3.0 canonical URI, ShareAlike
status, Radixor transformations, and Leo Galambos's protectable model-data contributions. The
notice does not claim ownership over the underlying data. GZip packaging and descriptor/checksum
generation are disclosed transformations; the in-memory trie is a Radixor runtime structure.
Each logical line describes one canonical stem and zero or more known word variants that should reduce to that stem. The format is intentionally lightweight, easy to maintain in source control, and directly consumable both by the programmatic loader and by the CLI compiler.
## Core structure
@@ -129,7 +150,11 @@ run running runs ran
## Character set, compression, and normalization
Dictionary files are read as UTF-8 text. Files loaded through `StemmerPatchTrieLoader.load(Path, ...)` may be either plain UTF-8 text or GZip-compressed UTF-8 text; the loader detects GZip input from the stream header instead of relying on the file extension. Bundled dictionaries are stored as GZip resources and are decoded as UTF-8 after decompression.
Dictionary files are read as UTF-8 text. Files loaded through `StemmerPatchTrieLoader.load(Path, ...)` may be either plain UTF-8 text or GZip-compressed UTF-8 text; the loader detects GZip input from the stream header instead of relying on the file extension. Registered model dictionaries are stored as GZip resources and are decoded as UTF-8 after decompression.
## Turn a dictionary into a model artifact
An arbitrary classpath copy is not a discoverable model. A model module places immutable input and its license under `models/<model-id>/src/modelInput/`, declares metadata and an independent version, and applies the model convention plugin. The build validates the input, copies identical bytes into a generated namespaced resource, generates `META-INF/radixor/models.index` and a descriptor, records SHA-256, and packages licensing material. See [Stemmer Models](stemmer-models.md#create-or-update-a-model-module) for the complete procedure and [Model Selection and Loading](model-selection-and-loading.md) for runtime use.
The parser and trie are not restricted to ASCII. Dictionary items are ordinary Java `String` values, and trie traversal works over Java `char` sequences. This supports Latin-script data with diacritics, Cyrillic data, Hebrew, Persian, Yiddish, and other scripts represented in UTF-8, subject to the normal Java `String` model and the projects traversal configuration.
@@ -235,3 +260,5 @@ To understand how those dictionary lines are transformed into compiled runtime a
- [CLI compilation](cli-compilation.md)
- [Programmatic usage](programmatic-usage.md)
- [Architecture and reduction](architecture-and-reduction.md)
!!! note "Radixor 4 model artifacts"
Language dictionaries are independently versioned runtime model artifacts, not resources embedded in `radixor`. Language-based APIs resolve deterministic defaults through `StemmerModelRegistry`; see [Stemmer Models](stemmer-models.md).

138
docs/fast-track.md Normal file
View File

@@ -0,0 +1,138 @@
# Fast Track
This page is the shortest path from an empty Java project to a working Radixor stemmer.
It deliberately uses an external model artifact and the preferred compiled-command runtime API, so the
first result does not require writing a dictionary, running the CLI compiler, or understanding
reduction internals.
Use this page when the goal is:
- add the dependency,
- load a registered language model,
- stem a token,
- know where to go next.
For deeper production guidance, see [Integration Deep Dive](integration-deep-dive.md).
## 1. Add The Dependency
Radixor is published as:
```text
groupId: org.egothor
artifactId: radixor
```
Radixor 4 is not yet represented by a published release in this working tree. Replace the version placeholder with the reviewed release you deploy.
For a Gradle project:
```kotlin
dependencies {
implementation("org.egothor:radixor:<radixor-version>")
runtimeOnly("org.egothor:radixor-model-us-uk-default:1.0.0")
}
```
For a Maven project:
```xml
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor</artifactId>
<version>${radixor.version}</version>
</dependency>
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor-model-us-uk-default</artifactId>
<version>1.0.0</version>
<scope>runtime</scope>
</dependency>
```
Radixor targets modern Java and has a dependency-light runtime core. The project documentation and
benchmarks assume a current JDK; Java 21 or newer is the practical baseline for current releases.
## 2. Load An External Model Dictionary
The fastest path is to use a registered model through `StemmerPatchTrieLoader.Language`.
This example uses `US_UK`, whose default ID is `us-uk-default`; the runtime model dependency above must be present.
```java
import java.io.IOException;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class RadixorFirstStem {
private RadixorFirstStem() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<CompiledPatchCommand> stemmer = StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final String token = "running";
final CompiledPatchCommand command = stemmer.get(token);
final String stem = command == null ? token : command.apply(token);
System.out.println(token + " -> " + stem);
}
}
```
The loaded `FrequencyTrie<CompiledPatchCommand>` has no mutating API. Load it once during application startup, publish it safely through application-owned lifecycle code, and reuse it for indexing and query processing.
## 3. Choose a Language Default or Explicit Model
Language defaults are exposed as enum constants. Common examples:
| Language | Enum constant |
| --- | --- |
| English | `US_UK` |
| German | `DE_DE` |
| French | `FR_FR` |
| Spanish | `ES_ES` |
| Italian | `IT_IT` |
| Polish | `PL_PL` |
| Russian | `RU_RU` |
| Czech | `CS_CZ` |
The full list, writing-direction notes, and benchmark links are in
[Built-in Languages](built-in-languages.md).
Polish has two models. `Language.PL_PL` selects `pl-pl-unimorph`; load the alternative explicitly with `StemmerPatchTrieLoader.loadCompiled("pl-pl-polimorf", true, reductionMode)`, or retain a registry and pass `registry.require("pl-pl-polimorf")` to the descriptor overload. See [Model Selection and Loading](model-selection-and-loading.md). Full PoliMorf construction requires substantially more startup heap than ordinary models; the repository verifies it in a dedicated 6 GiB test JVM.
## 4. Use The Same Stemmer On Both Sides
For search, use the same Radixor configuration during indexing and query processing. A typical
minimal integration flow is:
1. tokenize text with your application or search platform,
2. normalize tokens consistently,
3. call `stemmer.get(token)`,
4. apply the returned `CompiledPatchCommand`,
5. index or query with the resulting stem.
Do not load the trie per token. The compiled trie is the runtime artifact; per-token work should be
limited to lookup and patch application.
## 5. Next Step For Production
The fast path parses and compiles a registered model dictionary during startup. That is convenient for evaluation and
small services. For larger deployments, compile once, persist a `.radixor.gz` artifact, and load
that binary artifact at runtime.
Continue with:
- [Integration Deep Dive](integration-deep-dive.md) for production lifecycle guidance.
- [Loading and Building Stemmers](programmatic-loading-and-building.md) for all loading APIs.
- [Model Selection and Loading](model-selection-and-loading.md) for model dependencies, variants, and failures.
- [Built-in Languages](built-in-languages.md) for defaults and optional variants.
- [Benchmarking](benchmarking.md) for speed and quality interpretation.

View File

@@ -28,10 +28,26 @@ Radixor delivers:
Radixor is intended for teams that require consistent stemming quality at scale, while retaining the ability to evolve lexical resources after compilation and to handle ambiguous reductions with greater precision than traditional single-stem pipelines allow.
## Add the core and model data
The core `org.egothor:radixor` JAR contains no language dictionary. A minimal application adds one model; broad deployments may use the optional standard pack:
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
}
```
`StemmerPatchTrieLoader.loadCompiled(Language.PL_PL, ...)` resolves the default `pl-pl-unimorph`. `pl-pl-polimorf` is a separate optional model selected by stable model ID. Follow [Model Selection and Loading](model-selection-and-loading.md) for runnable examples or choose artifacts from the generated [model catalog](stemmer-model-catalog.md).
## Start here
- Read [Quick Start](quick-start.md) for immediate implementation guidance.
- Use [Programmatic Usage](programmatic-usage.md) for application integration patterns.
- Review [Benchmarking](benchmarking.md) for reproducible performance methodology.
- Read [Fast Track](fast-track.md) when you want the shortest path to a working bundled stemmer.
- Use [Model Selection and Loading](model-selection-and-loading.md) for default, explicit, dual-model, and ClassLoader examples.
- Use [Integration Deep Dive](integration-deep-dive.md) when you are wiring Radixor into a real application or search pipeline.
- Read [Quick Start](quick-start.md) for the broader developer walkthrough after the first result works.
- Use [Built-in Languages](built-in-languages.md) to interpret language defaults and optional model variants.
- Review [Benchmarking](benchmarking.md) and [Benchmark Results](benchmarks/index.md) for reproducible performance and quality methodology.
- Open [CI Reports](reports.md) to inspect published build artifacts and quality metrics.
- See the historical paper: [*Lemmatizer for Document Information Retrieval Systems in JAVA*](https://www.researchgate.net/publication/221512865_Lemmatizer_for_Document_Information_Retrieval_Systems_in_JAVA).

View File

@@ -0,0 +1,229 @@
# Integration Deep Dive
This page explains how to integrate Radixor into a real Java application after the first
fast-track experiment works. It covers dependencies, external model artifacts, runtime lifecycle,
deployment artifacts, and the decisions that matter in search or text-processing systems.
## Integration Model
Radixor has two separate phases:
| Phase | Work | Typical location |
| --- | --- | --- |
| Preparation | Parse dictionaries, derive patch commands, reduce and contract the trie, optionally persist a binary artifact. | Build pipeline, packaging job, admin tool, or startup for small services. |
| Runtime | Load an immutable compiled trie, look up patch commands, apply them to tokens. | Search indexing, query processing, text normalization, enrichment pipelines. |
The practical rule is simple: compile rarely, stem often.
For production systems, prefer a startup-owned or dependency-injected
`FrequencyTrie<CompiledPatchCommand>` per language/configuration. The compiled structure has no
mutating API. The project does not currently publish a formal cross-thread safety guarantee, so
applications should use normal safe-publication practices when sharing a loaded trie.
## Dependency Coordinates
The Maven coordinates are:
```text
org.egothor:radixor
```
Gradle:
```kotlin
dependencies {
implementation("org.egothor:radixor:<radixor-version>")
runtimeOnly("org.egothor:radixor-models-standard:<catalog-version>")
}
```
Maven:
```xml
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor</artifactId>
<version>${radixor.version}</version>
</dependency>
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor-models-standard</artifactId>
<version>${model.catalog.version}</version>
<scope>runtime</scope>
</dependency>
```
Replace the example versions with the independently selected core and catalog releases for your deployment.
The core Java module is:
```java
module org.egothor.radixor;
```
A named consuming module declares:
```java
module example.search {
requires org.egothor.radixor;
}
```
## Runtime Model Artifacts
The core ships no language dictionary. Add one or more `radixor-model-<model-id>` artifacts, or the optional metadata-only standard pack. Each model JAR contains an indexed descriptor and a namespaced GZip dictionary. `StemmerPatchTrieLoader.Language` represents language properties and a stable default model ID; it does not own embedded data.
The standard option is specifically a POM-only runtime dependency aggregate, not an all-model binary JAR. It resolves one default model JAR per language and excludes optional PoliMorf. The separate POM-only `radixor-models-bom` manages recommended versions without adding runtime artifacts. Repository tests and JMH attach individual model projects directly to non-production configurations, so neither path changes the root publication's dependency graph.
For minimal deployments choose only required model artifacts. For multiple Polish variants add both `pl-pl-unimorph` and `pl-pl-polimorf`, retain UniMorph as the language default, and request PoliMorf explicitly. See [Model Selection and Loading](model-selection-and-loading.md) for complete dependencies and [Built-in Languages](built-in-languages.md) for mappings.
Use `loadCompiled("pl-pl-polimorf", true, reductionMode)` for direct exact selection, or discover once and call `loadCompiled(descriptor, true, reductionMode)`. Neither form caches the trie. Complete PoliMorf startup is memory-intensive and is verified with a dedicated 6 GiB heap; construct it once during application initialization and retain the immutable result.
## Minimal Service Wrapper
A small service wrapper keeps loading, null handling, and fallback behavior in one place.
```java
import java.io.IOException;
import java.util.Objects;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class RadixorStemmerService {
private final FrequencyTrie<CompiledPatchCommand> trie;
public RadixorStemmerService(final StemmerPatchTrieLoader.Language language) throws IOException {
this.trie = StemmerPatchTrieLoader.loadCompiled(
Objects.requireNonNull(language, "language"),
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
}
public String stem(final String token) {
final String checkedToken = Objects.requireNonNull(token, "token");
final CompiledPatchCommand command = trie.get(checkedToken);
return command == null ? checkedToken : command.apply(checkedToken);
}
}
```
The fallback behavior preserves the original token when the trie has no patch command for it. That
is usually the right default for search normalization, because unknown tokens should remain
searchable.
## Production Artifact Workflow
For a controlled deployment, compile once and deploy the binary artifact:
1. choose a registered model resource or caller-owned custom dictionary,
2. optionally extend it with domain vocabulary,
3. compile a contracted trie,
4. persist it as `.radixor.gz`,
5. deploy that artifact with the application,
6. load it with `StemmerPatchTrieLoader.loadBinaryCompiled(...)`.
Runtime loading then avoids dictionary parsing and preparation-time memory pressure.
```java
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class BinaryStemmerLoader {
private BinaryStemmerLoader() {
throw new AssertionError("No instances.");
}
public static FrequencyTrie<CompiledPatchCommand> loadEnglish() throws IOException {
return StemmerPatchTrieLoader.loadBinaryCompiled(Path.of("stemmers", "english.radixor.gz"));
}
}
```
Use [CLI Compilation](cli-compilation.md) for command-line artifact creation, or
[Extending and Persisting Compiled Tries](programmatic-extending-and-persistence.md) for
programmatic artifact generation.
## Search Pipeline Guidance
Use Radixor consistently across indexing and querying:
- choose one language dictionary per field or per analysis chain,
- apply the same token normalization before stemming on both sides,
- keep the compiled trie in memory and reuse it,
- use `get(...)` for a single preferred stem,
- use `getAll(...)` when a retrieval model benefits from preserving alternatives,
- version custom `.radixor.gz` artifacts with the application or index schema.
For multilingual content, do not run every token through every language. Route text by field,
document metadata, or language detection before stemming.
## Choosing Registered Versus Custom Dictionaries
Start with registered model artifacts when:
- the language is supported,
- the application needs a strong baseline quickly,
- the vocabulary is general-purpose,
- the team is evaluating Radixor or building an initial integration.
Use custom or extended dictionaries when:
- domain vocabulary changes search quality,
- product names, technical terms, legal terms, or biomedical terms must be preserved consistently,
- stemming behavior must be curated and reviewed,
- a release process needs a versioned lexical artifact.
The dictionary format is intentionally simple and documented in
[Dictionary Format](dictionary-format.md). Contribution standards are described in
[Contributing Dictionaries](contributing-dictionaries.md).
## Performance Practices
The hot path should be only:
```text
token -> trie lookup -> compiled command application -> stem
```
Avoid these patterns in production request paths:
- loading or compiling dictionaries per request,
- applying serialized patch strings repeatedly instead of `CompiledPatchCommand`,
- rebuilding tries for short-lived batches,
- mixing different stemmer configurations between indexing and querying,
- interpreting speed without checking exact-root quality.
The current benchmark documentation separates methodology, corpora, environment, and language
results so performance claims remain auditable. Start with [Benchmarking](benchmarking.md), then
use [Benchmark Results](benchmarks/index.md) for the detailed reference tree.
## Operational Checklist
Before production rollout:
- dependency version is pinned,
- language, model ID, model artifact version, checksum, and reduction mode are documented,
- indexing and query pipelines use the same stemming configuration,
- custom artifacts are versioned and reproducible,
- fallback behavior for unknown tokens is explicit,
- benchmark expectations are read together with quality metrics,
- CI includes at least a smoke test that stems representative project vocabulary.
## Related Pages
- [Fast Track](fast-track.md)
- [Quick Start](quick-start.md)
- [Built-in Languages](built-in-languages.md)
- [Programmatic Usage](programmatic-usage.md)
- [Model Selection and Loading](model-selection-and-loading.md)
- [CLI Compilation](cli-compilation.md)
- [Benchmarking](benchmarking.md)

View File

@@ -6,7 +6,10 @@ Compiled trie nodes (`CompiledNode`) use three lookup strategies when resolving
2. linear scan for very small child counts,
3. binary search over sorted edge labels.
This page explains the dense path, what `maxExpandedIndex` controls, and how to tune it.
This page explains the dense path, what `maxExpandedIndex` controls, and how to tune it. These
edge lookup strategies operate after trie reduction and uniform-subtree contraction. If lookup
reaches an accepting contracted leaf, no child edge search is needed for the remaining input
characters.
## Runtime model of one node
@@ -75,7 +78,7 @@ The value never changes lookup semantics. It only changes the in-memory structur
This threshold is **not** stored in `TrieMetadata`.
- The binary format stores only trie payload and semantic metadata (`reduction`, `traversal`,
case/diacritic settings, and stream version).
case/diacritic settings, contraction settings, and stream version).
- `maxExpandedIndex` is chosen when materializing nodes in memory.
- You can therefore keep one persisted artifact and load it with different in-memory
trade-offs depending on deployment constraints.
@@ -135,15 +138,17 @@ At artifact load time, you can tune the same trade-off independently of persiste
```java
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
var defaultLookup = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> defaultLookup = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"));
var fastLookup = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> fastLookup = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"), 1024);
var compactLookup = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> compactLookup = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"), 0);
```

View File

@@ -0,0 +1,298 @@
# Migration and Backward Compatibility
## Radixor 3.x to 4.x architecture migration
Radixor 3.x published algorithm classes and language dictionaries together as `org.egothor:radixor`. Radixor 4 keeps that established coordinate for the algorithmic core but removes every dictionary from the core JAR. Applications must now choose independently versioned model artifacts. This is deliberately source-compatible where practical and deliberately different at runtime.
### Before and after: dependencies
| Deployment | 3.x | 4.x |
|---|---|---|
| Core | `org.egothor:radixor:<3.x-version>` included dictionaries | `org.egothor:radixor:<radixor-version>` contains code only |
| Minimal Polish | No separate data dependency | Add `radixor-model-pl-pl-unimorph:1.0.0` |
| All defaults | Implicitly embedded | Add optional `radixor-models-standard:<catalog-version>` |
| Optional Polish variant | Not independently selectable | Add and explicitly select `radixor-model-pl-pl-polimorf:1.0.0` |
Gradle, preserving the previous Polish default:
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
}
```
Gradle, broad default coverage:
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-models-standard:<catalog-version>'
}
```
Maven, preserving the Polish default:
```xml
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor</artifactId>
<version>${radixor.version}</version>
</dependency>
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor-model-pl-pl-unimorph</artifactId>
<version>1.0.0</version>
<scope>runtime</scope>
</dependency>
```
### Before and after: API behavior
Language-oriented calls remain source-compatible:
```java
final FrequencyTrie<CompiledPatchCommand> polish =
StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.PL_PL,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
In 4.x this call creates a registry and resolves `Language.PL_PL.defaultModelId()`, which is `pl-pl-unimorph`. Source compatibility does not imply runtime classpath compatibility: the call fails with `StemmerModelNotFoundException` unless that model is visible.
Explicit selection enables multiple variants:
```java
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor polimorf = registry.require("pl-pl-polimorf");
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadCompiled(
polimorf,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
The existing `load(String, ...)` overload means a filesystem path. The compiled `loadCompiled(String, boolean, ReductionMode)` overload now means a stable model ID; use the `Path` overload for a filesystem dictionary. Descriptor-based compiled loading avoids rediscovery when an application retains a registry. See [Model Selection and Loading](model-selection-and-loading.md) for complete examples.
### Polish migration scenarios
1. **Preserve previous default behavior:** add `radixor-model-pl-pl-unimorph` and keep using `Language.PL_PL`.
2. **Use PoliMorf:** add `radixor-model-pl-pl-polimorf` and call `registry.require("pl-pl-polimorf")`.
3. **Deploy both:** add both runtime artifacts and load each descriptor by ID. They are not merged.
4. **Verify selection:** compare `registry.requireDefault(Language.PL_PL).id()` with `pl-pl-unimorph` through normal application control flow or a JUnit assertion, and inspect `registry.findByLanguage(Language.PL_PL)`.
5. **Diagnose absence:** read the exact `StemmerModelNotFoundException` message, then inspect the production `runtimeClasspath` rather than changing dependency order.
UniMorph and PoliMorf are not interchangeable quality datasets. They can differ in vocabulary, provenance, licensing, and stemming outputs.
Model migration does not erase source obligations. Each migrated UniMorph artifact packages its
language-specific notice with upstream attribution, Radixor modifications and contribution
statement, ShareAlike terms, and the canonical CC BY-SA 3.0 URI. The original imports did not
record exact UniMorph commits, so descriptors use
`source.revision=not-recorded-in-legacy-import` and disclose that fact. Future model imports must
record an exact upstream revision and source-archive checksum.
### Compatibility table
| Dimension | 4.x migration status |
|---|---|
| Source compatibility | Language-oriented loader signatures remain; external model dependencies are new |
| Binary compatibility | Removing resources is a major-version boundary; review all deployed artifacts |
| Runtime classpath | At least one selected model JAR is required |
| Model format | Descriptor format `radixor-dictionary-tsv-gzip` version `1` is validated by the registry |
| Model IDs | Stable runtime identities, independent of artifact discovery order |
| Core Maven coordinate | Remains `org.egothor:radixor` |
| Release versions | Core, each model, upstream source, format, and catalog versions evolve separately |
### Upgrade checklist
- Update the core dependency.
- Choose individual model artifacts or the standard pack.
- Put resource-only model dependencies on the production runtime classpath.
- Verify `Language.defaultModelId()` mappings used by the application.
- Inspect shaded, minimized, plugin, or modular packaging for indexes and resources.
- Run application-level vocabulary and output regression tests.
- Track model artifact versions and checksums separately from the core version.
### Roll back model choice
To return from optional PoliMorf to the default UniMorph behavior, add or retain `radixor-model-pl-pl-unimorph`, stop requesting `pl-pl-polimorf`, and load `Language.PL_PL` or explicitly request `pl-pl-unimorph`. Do not change the language constant. Remove the unused PoliMorf runtime dependency after verifying no explicit lookup still needs it.
Rolling the whole application back to 3.x instead requires restoring the reviewed 3.x core dependency and removing 4.x model assumptions. Do not combine 3.x embedded resources with the 4.x registry architecture.
Core, model, and catalog releases are independent:
```bash
git tag -a "release@4.0.0" -m "Release Radixor 4.0.0"
git tag -a "model/pl-pl-polimorf@1.0.0" -m "Release Polish PoliMorf model 1.0.0"
git tag -a "models-catalog@2026.1" -m "Release Radixor model catalog 2026.1"
```
A core tag publishes only the root `org.egothor:radixor` software artifacts, never model JARs. A model tag validates and publishes exactly its matching module, never core, standard, BOM, JMH, or the multilingual quality suite. A catalog tag publishes only BOM and standard aggregate metadata. Local model dry-run:
The catalog artifacts are POM-only: `radixor-models-standard` carries runtime dependencies on the 20 defaults, while `radixor-models-bom` carries dependency-management constraints for all 21 individual models. Neither publishes an empty binary, sources, or Javadoc JAR. This Maven BOM is distinct from the root CycloneDX SBOM report under `build/reports/sbom/`.
```bash
./tools/parse-model-release-tag.sh "model/pl-pl-polimorf@1.0.0" .
./gradlew --no-daemon :models:pl-pl-polimorf:check
./gradlew --no-daemon :models:pl-pl-polimorf:validateModelRelease -PmodelReleaseVersion=1.0.0
./gradlew --no-daemon :models:pl-pl-polimorf:packageModelReleaseCandidate -PmodelReleaseVersion=1.0.0
```
Model format compatibility is descriptor-level and does not alter migrated bytes. Version 1 is `radixor-dictionary-tsv-gzip`. Model versions come from each module's `model-version.txt` or the matching explicit release property; catalog version comes from `models/catalog-version.txt`; only core uses Git-derived `release@` versioning.
The model catalog used by the published documentation is generated under `build/mkdocs-source/`. Neither generated Markdown nor rendered MkDocs output belongs in Git.
The remainder of this page describes the earlier migration from repeated serialized patch-command application to compiled patch commands.
## Summary
Radixor patch commands are still encoded as compact strings when dictionaries are built and persisted. That serialized form remains the interchange format used by textual dictionaries, binary artifacts, and compilation tooling.
Runtime stemming should no longer repeatedly apply those serialized strings directly. Since 2.3.0, the String-based patch application API is deprecated. Code that stems live input should load or create `CompiledPatchCommand` values and reuse them. The deprecated API remains available for compatibility during the transition, but applications should migrate before 3.0.0.
The reason is performance. The old API parses the serialized P-command every time it is applied. `CompiledPatchCommand` parses it once and stores a concrete immutable command object, so repeated stemming avoids the same analysis work.
## Deprecated Runtime APIs
The following API family is kept for source compatibility but is no longer the preferred runtime path:
- `PatchCommandEncoder.apply(String, String)`
- `PatchCommandEncoder.apply(String, String, WordTraversalDirection)`
- `PatchCommandEncoder.applyTo(..., String, WordTraversalDirection, ...)`
- `PatchCommandEncoder.applyWithConfiguredDirection(String, String)`
- `StemmerPatchTrieLoader.load(...)` overloads returning `FrequencyTrie<String>`
- `StemmerPatchTrieLoader.loadBinary(...)` overloads returning `FrequencyTrie<String>`
Use the compiled equivalents for runtime stemming:
- `CompiledPatchCommand.compile(String, WordTraversalDirection)`
- `PatchCommandEncoder.compile(String)`
- `PatchCommandEncoder.compile(String, WordTraversalDirection)`
- `StemmerPatchTrieLoader.loadCompiled(...)`
- `StemmerPatchTrieLoader.loadBinaryCompiled(...)`
## Loading A Text Dictionary
Old runtime code:
```java
Path dictionary = Path.of("dictionary.txt");
ReductionSettings settings = ReductionSettings.withDefaults(
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(dictionary, true, settings);
String word = "running";
String patch = trie.get(word);
String stem = patch == null
? word
: PatchCommandEncoder.apply(word, patch, trie.traversalDirection());
```
New runtime code:
```java
Path dictionary = Path.of("dictionary.txt");
ReductionSettings settings = ReductionSettings.withDefaults(
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(dictionary, true, settings);
String word = "running";
CompiledPatchCommand patch = trie.get(word);
String stem = patch == null ? word : patch.apply(word);
```
## Loading A Binary Artifact
Old runtime code:
```java
FrequencyTrie<String> trie = StemmerPatchTrieLoader.loadBinary(Path.of("us-uk.radixor.gz"));
String word = "studies";
String patch = trie.get(word);
String stem = patch == null
? word
: PatchCommandEncoder.apply(word, patch, trie.traversalDirection());
```
New runtime code:
```java
FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadBinaryCompiled(Path.of("us-uk.radixor.gz"));
String word = "studies";
CompiledPatchCommand patch = trie.get(word);
String stem = patch == null ? word : patch.apply(word);
```
Existing binary artifacts remain readable. `loadBinaryCompiled(...)` reads the stored serialized patch strings and compiles them during load setup, before live stemming begins.
## Manual Patch Encoding
Encoding still produces a serialized patch command because that is the compact stored representation:
```java
PatchCommandEncoder encoder = PatchCommandEncoder.builder().build();
String patch = encoder.encode("running", "run");
```
Old repeated application:
```java
String stem = PatchCommandEncoder.apply("running", patch);
```
New repeated application:
```java
CompiledPatchCommand compiled = encoder.compile(patch);
String stem = compiled.apply("running");
```
## Caller-Owned Output Buffers
Old buffer-oriented code:
```java
char[] output = new char[32];
int length = PatchCommandEncoder.applyTo(
"running",
patch,
WordTraversalDirection.BACKWARD,
output,
0,
output.length);
```
New buffer-oriented code:
```java
CompiledPatchCommand compiled = CompiledPatchCommand.compile(patch, WordTraversalDirection.BACKWARD);
char[] output = new char[32];
int length = compiled.applyTo("running", output, 0, output.length);
```
Both APIs return `CompiledPatchCommand.APPLY_INSUFFICIENT_CAPACITY` when the caller-owned output range is too small.
## Compatibility Rules
Serialized patch strings remain part of the dictionary and artifact format. The deprecation is about repeated runtime application of serialized strings, not about the stored representation itself.
Compatibility tests may continue to exercise the deprecated API to prove that old artifacts and source code still work during the transition. New production code, examples, and benchmark runtime paths should use `CompiledPatchCommand`.
The command-line compiler still writes artifacts containing serialized patch commands. Runtime loaders can expose those commands as compiled immutable objects through `loadCompiled(...)` and `loadBinaryCompiled(...)`.
## Contracted Trie Artifacts
Current compiled loaders and freshly written binary artifacts can use contracted compiled tries.
Contraction replaces a subtree with an accepting leaf when every reachable entry below that subtree
selects the same preferred patch command. This changes the physical trie shape and the binary
stream version, but it does not change the serialized patch-command language.
Existing binary artifacts remain readable through the compatibility reader. To obtain the
contracted runtime representation, rebuild the artifact with the current compiler or load the
source dictionary through the current `loadCompiled(...)` APIs. Applications that only consume
`CompiledPatchCommand` values through `get()` and `apply(...)` do not need code changes for this
optimization.

View File

@@ -0,0 +1,277 @@
# Model Selection and Loading
Radixor separates executable stemming code from language data. The core artifact supplies dictionary parsing, trie construction, patch commands, lookup, and the model registry. A model artifact supplies one indexed descriptor, one GZip-compressed Radixor dictionary, and its licensing material. The core JAR contains no language dictionary.
```text
Application
-> org.egothor:radixor (algorithmic core)
-> StemmerModelRegistry
-> indexed model descriptor
-> namespaced stemmer.gz resource
-> checksum verification and dictionary parsing
-> FrequencyTrie construction
-> patch lookup and stemming
```
## Language and model ID
These identifiers answer different questions:
| Concept | Example | Meaning |
|---|---|---|
| Language | `Language.PL_PL` | Polish as a linguistic identity |
| Model ID | `pl-pl-unimorph` | One concrete Polish model configuration |
| Model ID | `pl-pl-polimorf` | A different concrete Polish model configuration |
| Default model | `PL_PL -> pl-pl-unimorph` | The model selected by the language convenience API |
One language can have several models. `Language.PL_PL` is neither UniMorph nor PoliMorf. `loadCompiled(Language.PL_PL, ...)` resolves the stable default ID declared by `Language.defaultModelId()`. An explicit lookup requests exactly one ID. Registry ordering never changes either decision.
Licensing follows the selected artifact. Radixor Java software is BSD-3-Clause; UniMorph-derived
model data carries a model-specific CC BY-SA 3.0 notice, while PoliMorf carries its separate
BSD-2-Clause license. The UniMorph notice preserves upstream attribution and identifies the
Radixor transformations and limited protectable contributions without claiming the underlying data.
## Choose runtime dependencies
Radixor 4 is an architectural migration that is not yet represented by a published release in this working tree, so core and catalog versions below use placeholders. Every source-controlled model currently has model version `1.0.0`.
### Core plus the default Polish model
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
}
```
### Core plus optional PoliMorf
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-polimorf:1.0.0'
}
```
This dependency makes `pl-pl-polimorf` discoverable; it does not change the default for `PL_PL`.
### Both Polish models
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
runtimeOnly 'org.egothor:radixor-model-pl-pl-polimorf:1.0.0'
}
```
### Standard defaults
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-models-standard:<catalog-version>'
}
```
The standard aggregate is POM-only. Its POM supplies exactly one default model per supported language as transitive runtime dependencies and excludes optional PoliMorf. It publishes no empty binary JAR.
### BOM-managed versions
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
implementation platform('org.egothor:radixor-models-bom:<catalog-version>')
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph'
runtimeOnly 'org.egothor:radixor-model-pl-pl-polimorf'
}
```
Equivalent Maven dependencies use ordinary runtime scope:
```xml
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor</artifactId>
<version>${radixor.version}</version>
</dependency>
<dependency>
<groupId>org.egothor</groupId>
<artifactId>radixor-model-pl-pl-unimorph</artifactId>
<version>1.0.0</version>
<scope>runtime</scope>
</dependency>
```
Use `implementation` for the core because application code imports its API. Models normally use `runtimeOnly` because they provide resources rather than Java types. Tests with a deliberately isolated model set use `testRuntimeOnly`. The repository attaches every default model and optional PoliMorf directly to `jmhRuntimeOnly`; test and quality configurations likewise use direct non-production model dependencies. No benchmark aggregate artifact exists, and no model dependency enters the root published POM.
## Load the documented default
Dependency prerequisite: core plus `radixor-model-pl-pl-unimorph` (or the standard pack).
```java
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.ReductionSettings;
import org.egothor.stemmer.StemmerPatchTrieLoader;
final FrequencyTrie<CompiledPatchCommand> polish =
StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.PL_PL,
true,
ReductionSettings.withDefaults(
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS));
final String word = "koty";
final CompiledPatchCommand patch = polish.get(word);
final String stem = patch == null ? word : patch.apply(word);
```
The loader creates a registry from the thread context class loader, resolves `PL_PL` to `pl-pl-unimorph`, verifies the compressed resource checksum, decompresses and parses the UTF-8 dictionary, constructs the trie, and compiles its patch commands. It does not load a serialized Java object. If the default artifact is absent, `StemmerModelNotFoundException` names the missing ID and suggested Maven artifact.
## Load PoliMorf explicitly
Dependency prerequisite: core plus `radixor-model-pl-pl-polimorf`.
```java
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerModelDescriptor;
import org.egothor.stemmer.StemmerModelRegistry;
import org.egothor.stemmer.StemmerPatchTrieLoader;
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor descriptor = registry.require("pl-pl-polimorf");
final FrequencyTrie<CompiledPatchCommand> polish =
StemmerPatchTrieLoader.loadCompiled(
descriptor,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final String word = "koty";
final CompiledPatchCommand patch = polish.get(word);
final String stem = patch == null ? word : patch.apply(word);
```
The equivalent direct model-ID form is:
```java
final FrequencyTrie<CompiledPatchCommand> polimorf =
StemmerPatchTrieLoader.loadCompiled(
"pl-pl-polimorf",
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
`require("pl-pl-polimorf")` and the direct overload are deterministic because registry keys are stable model IDs. Discovery order is sorted, duplicate IDs are rejected, and no “first Polish model on the classpath” fallback exists. Both overloads return compiled patch-command values and perform complete integrity checking, parsing, reduction, and trie construction.
!!! warning "PoliMorf startup memory"
Full construction of the PoliMorf model is memory-intensive. Radixor verifies it in one isolated JVM with a task-specific maximum heap of 6 GiB. Two measured verification runs completed full construction in 23.7 seconds and 23.5 seconds, producing 358,993 canonical trie nodes; the complete Gradle processes peaked at approximately 6.23 GiB resident memory. The compressed model is only 12,624,997 bytes (68,093,680 bytes decompressed), so JAR size is not a proxy for construction-time heap. Applications loading the complete model must provision sufficient startup heap. Radixor does not currently expose a measured retained-heap value, so do not infer one from the process peak.
## Use both Polish models
Dependency prerequisite: both Polish model artifacts.
```java
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor unimorph = registry.require("pl-pl-unimorph");
final StemmerModelDescriptor polimorf = registry.require("pl-pl-polimorf");
final StemmerModelDescriptor defaultPolish =
registry.requireDefault(StemmerPatchTrieLoader.Language.PL_PL);
if (!"pl-pl-unimorph".equals(defaultPolish.id())) {
throw new IllegalStateException(
"Unexpected default Polish model: " + defaultPolish.id());
}
final FrequencyTrie<CompiledPatchCommand> unimorphTrie =
StemmerPatchTrieLoader.loadCompiled(unimorph, true, reductionMode);
final FrequencyTrie<CompiledPatchCommand> polimorfTrie =
StemmerPatchTrieLoader.loadCompiled(polimorf, true, reductionMode);
```
The descriptors and tries coexist independently. The models are not merged, and adding PoliMorf does not alter the language default. An application that compares, votes across, or merges model outputs must implement that higher-level policy explicitly.
## Discover available models
```java
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
for (final StemmerModelDescriptor model : registry.models()) {
System.out.printf("%s %s %s %s/%d descriptor=%s%n",
model.id(), model.language(), model.version(),
model.format(), model.formatVersion(), model.source());
}
final java.util.List<StemmerModelDescriptor> polishModels =
registry.findByLanguage(StemmerPatchTrieLoader.Language.PL_PL);
```
Both lists use stable model-ID order. The public descriptor API exposes ID, model artifact version, language, display name, runtime resource, default flag, format, format version, checksum, and descriptor source URL. Packaged provenance properties such as `source.name` and `source.version` are not currently exposed as typed descriptor accessors; consult the generated [model catalog](stemmer-model-catalog.md) for them.
## Use an explicit ClassLoader
```java
final ClassLoader pluginLoader = plugin.getClass().getClassLoader();
final StemmerModelRegistry pluginModels =
StemmerModelRegistry.fromClassLoader(pluginLoader);
final StemmerModelDescriptor model = pluginModels.require("pl-pl-polimorf");
```
`fromContextClassLoader()` uses the current thread context loader, falling back to Radixor's defining loader when the context loader is `null`. `fromClassLoader(loader)` searches only what that loader can expose through `getResources(...)` and ordinary resource lookup. Plugin containers, application servers, and isolated tests can therefore observe different model sets. Pass a non-null loader and retain the registry associated with that deployment scope.
## Error handling
```java
try {
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor model = registry.require("pl-pl-polimorf");
// Load and cache the trie during application startup.
} catch (final StemmerModelNotFoundException exception) {
// Missing runtime dependency or model hidden from this ClassLoader.
throw exception;
} catch (final DuplicateStemmerModelException exception) {
// Conflicting artifacts or a fat JAR duplicated one stable ID.
throw exception;
} catch (final UnsupportedStemmerModelFormatException exception) {
// The model format or format version is not supported by this core.
throw exception;
} catch (final StemmerModelIntegrityException exception) {
// Malformed descriptor/index, missing resource, wrong language, or checksum failure.
throw exception;
} catch (final java.io.IOException exception) {
// Classpath enumeration or resource I/O failed.
throw new java.io.UncheckedIOException(exception);
}
```
Malformed metadata does not have a separate public exception: it is reported as `StemmerModelIntegrityException`. Missing explicit and default models both use `StemmerModelNotFoundException`; the default diagnostic additionally names the language and expected default ID. Never swallow these failures or choose an arbitrary model.
## Lifecycle and concurrency
`StemmerModelRegistry` copies discovered descriptors into an unmodifiable map, returns immutable list copies, and has no mutating API. `StemmerModelDescriptor` is final with final fields. These objects are safe to retain after discovery. Registry discovery is not globally cached: every call enumerates indexes and parses descriptors again. Model loading is also not cached: every call reads, hashes, decompresses, parses, and builds a new trie.
Compiled tries are immutable and thread-safe for concurrent reads. Load a registry and the required tries once during application startup, publish them safely, and reuse them. The loader does not cache model tries; do not repeatedly discover and compile models per token. When comparing both Polish models, account for the memory of two independent tries and avoid constructing them concurrently unless the deployment is sized for that peak.
## Troubleshooting
| Symptom | Meaning | Action |
|---|---|---|
| `No default model '...' is available` | The default artifact is absent from the selected loader | Add the named model as a runtime dependency and inspect `runtimeClasspath` |
| `No model 'pl-pl-polimorf' is available` | Explicit optional model is absent or invisible | Add `radixor-model-pl-pl-polimorf` to runtime, not only tests |
| Duplicate model ID | Two resources declare one stable ID | Remove the duplicate artifact or fix fat-JAR resource duplication; do not reorder the classpath |
| Checksum mismatch | Descriptor and compressed bytes differ | Replace the corrupted or incorrectly repackaged artifact |
| Unsupported format | Core supports neither the format name nor version | Use a compatible core/model pair; do not bypass validation |
| Works in tests, fails in production | The model is probably `testRuntimeOnly` | Inspect `./gradlew dependencies --configuration runtimeClasspath` |
| Visible with one loader only | Class loaders expose different resources | Call `fromClassLoader(...)` with the loader that owns the model JAR |
| PoliMorf is installed but language loading uses UniMorph | Expected default behavior | Select `pl-pl-polimorf` explicitly |
| Dependency minimization removed the model | Resource-only dependency was treated as unused | Preserve the model JAR, index, descriptor, license, and dictionary |
| Shaded JAR fails or reports duplicates | Indexes/resources were dropped or duplicated | Inspect with `jar tf app.jar | grep -E 'models.index|stemmer.gz'`; configure deterministic resource merging without duplicating IDs |
Useful Gradle diagnostics include `./gradlew dependencyInsight --dependency radixor-model --configuration runtimeClasspath` and `./gradlew dependencies --configuration testRuntimeClasspath`. Classpath order is not a remediation mechanism.
Continue with [Programmatic Usage](programmatic-usage.md), [Stemmer Models](stemmer-models.md), [Built-in Languages](built-in-languages.md), the generated [model catalog](stemmer-model-catalog.md), and [Architecture](architecture.md).

View File

@@ -94,7 +94,8 @@ This model works especially well when domain-specific extensions are added in la
After loading a compiled artifact, applications can inspect the persisted build descriptor directly:
```java
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.loadBinary("build/stemmers/cs_cz.dat.gz");
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadBinaryCompiled("build/stemmers/cs_cz.dat.gz");
final TrieMetadata metadata = trie.metadata();
System.out.println(metadata.formatVersion());

View File

@@ -2,25 +2,26 @@
This document explains how to acquire a compiled Radixor stemmer in Java.
## Load a bundled language dictionary
## Load a registered default model
Bundled language resources are simple to use and compile directly into a `FrequencyTrie<String>` during loading.
Language-oriented entry points resolve a registered default model and compile its GZip textual dictionary into a `FrequencyTrie<CompiledPatchCommand>`. The corresponding model JAR must be on the runtime classpath; the core contains no dictionary.
```java
import java.io.IOException;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class BundledLanguageExample {
public final class RegisteredLanguageModelExample {
private BundledLanguageExample() {
private RegisteredLanguageModelExample() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
@@ -30,14 +31,22 @@ public final class BundledLanguageExample {
The `storeOriginal` flag controls whether the canonical stem is inserted as a no-op patch entry for the stem itself.
Language-oriented `loadCompiled(...)` entry points build the runtime trie with the same contracted
representation used by the published benchmarks. During compilation, uniform preferred-command
subtrees are collapsed into accepting leaves, so lookup can stop before consuming the entire input
when the remaining characters cannot change the selected patch command.
## Load a textual dictionary
Loading from a dictionary file follows the same preparation model as bundled resources, but the source comes from your own file or path. The input may be plain UTF-8 text or GZip-compressed UTF-8 text; the loader detects GZip data from the stream header. The textual format is tab-separated values, meaning that columns are separated by the tab character. Each non-empty logical line starts with the stem column and may contain zero or more variant columns. Input case normalization is controlled by `CaseProcessingMode` (default: `LOWERCASE_WITH_LOCALE_ROOT`), trailing remarks introduced by `#` or `//` are ignored, and dictionary items containing embedded whitespace are currently ignored with warning-level diagnostics.
Loading from a dictionary file follows the same trie preparation model as registered model resources, but the source comes from your own file or path and bypasses registry metadata. The input may be plain UTF-8 text or GZip-compressed UTF-8 text; the loader detects GZip data from the stream header. The textual format is tab-separated values, meaning that columns are separated by the tab character. Each non-empty logical line starts with the stem column and may contain zero or more variant columns. Input case normalization is controlled by `CaseProcessingMode` (default: `LOWERCASE_WITH_LOCALE_ROOT`), trailing remarks introduced by `#` or `//` are ignored, and dictionary items containing embedded whitespace are currently ignored with warning-level diagnostics.
For explicit model IDs, multiple variants, and ClassLoader control, see [Model Selection and Loading](model-selection-and-loading.md).
```java
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.ReductionSettings;
@@ -50,7 +59,7 @@ public final class LoadTextDictionaryExample {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(
Path.of("data", "stemmer.tsv"),
true,
ReductionSettings.withDefaults(
@@ -59,7 +68,12 @@ public final class LoadTextDictionaryExample {
}
```
Additional `StemmerPatchTrieLoader.load(...)` overloads let callers provide explicit `WordTraversalDirection`, `CaseProcessingMode`, `DiacriticProcessingMode`, or a complete `TrieMetadata` instance. Use those overloads when a custom dictionary must be compiled with forward traversal for right-to-left languages, case-sensitive keys, or diacritic stripping.
Additional `StemmerPatchTrieLoader.loadCompiled(...)` overloads let callers provide explicit `WordTraversalDirection`, `CaseProcessingMode`, `DiacriticProcessingMode`, or a complete `TrieMetadata` instance. Use those overloads when a custom dictionary must be compiled with forward traversal for right-to-left languages, case-sensitive keys, or diacritic stripping.
When `ReductionSettings` are supplied through these compiled loader APIs, uniform-subtree
contraction is still enabled as an internal pre-reduction step. The public `ReductionMode` remains
the semantic policy for subtree equivalence after that contraction has removed regions whose
preferred command is already uniform.
## Load a compiled binary artifact
@@ -69,6 +83,7 @@ Binary loading is typically the preferred runtime path because it avoids reparsi
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
@@ -79,7 +94,7 @@ public final class LoadBinaryExample {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"));
}
}
@@ -96,6 +111,7 @@ at load time. This does not change persisted metadata, only the materialized in-
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
@@ -106,14 +122,14 @@ public final class LoadBinaryWithDenseLookupExample {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> balanced = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> balanced = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"));
final FrequencyTrie<String> fast = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> fast = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"),
1024);
final FrequencyTrie<String> compact = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> compact = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"),
0);
}

View File

@@ -10,7 +10,7 @@ This document explains how a compiled Radixor trie is queried and how ambiguity
```java
final String word = "running";
final String patch = trie.get(word);
final CompiledPatchCommand patch = trie.get(word);
```
### `getAll(...)`: ordered local values
@@ -18,7 +18,7 @@ final String patch = trie.get(word);
`FrequencyTrie.getAll(String)` returns all values stored at the addressed node, ordered by descending frequency using the same deterministic tie-breaking rules. The returned array is a defensive copy. If the key is missing or has no local values, an empty array is returned.
```java
final String[] patches = trie.getAll("axes");
final CompiledPatchCommand[] patches = trie.getAll("axes");
```
### `getEntries(...)`: values with counts
@@ -28,9 +28,10 @@ final String[] patches = trie.getAll("axes");
```java
import java.util.List;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.ValueCount;
final List<ValueCount<String>> entries = trie.getEntries("axes");
final List<ValueCount<CompiledPatchCommand>> entries = trie.getEntries("axes");
```
### Visitor lookup for hot paths
@@ -55,31 +56,29 @@ trie.getAllNormalized(token, 0, token.length, (patch, count, rank) -> {
`getAllNormalized(...)` bypasses `caseProcessingMode` and `diacriticProcessingMode`; callers are responsible for supplying canonical input. `maxResults == 0` visits nothing, negative values are rejected, and a sink returning `false` stops iteration after the current callback.
## Apply patch commands
## Apply compiled patch commands
A patch command is not the final stem. It must be applied to the original input token. `PatchCommandEncoder.apply(source, patchCommand)` performs that transformation directly on the serialized command format. If the source is `null`, the method returns `null`. If the patch is `null`, empty, or malformed in compatibility-relevant ways, the original source word is preserved. Equal source and target words are represented by the canonical no-op patch.
A patch command is not the final stem. It must be applied to the original input token. Runtime code should use `CompiledPatchCommand`, which parses the stored patch-command representation once during setup and then applies the concrete immutable command repeatedly.
```java
import org.egothor.stemmer.PatchCommandEncoder;
import org.egothor.stemmer.CompiledPatchCommand;
final String word = "running";
final String patch = trie.get(word);
final String stem = PatchCommandEncoder.apply(word, patch);
final CompiledPatchCommand patch = trie.get(word);
final String stem = patch == null ? word : patch.apply(word);
```
Hot paths can apply a patch into caller-owned character storage:
```java
final char[] output = new char[32];
final int produced = PatchCommandEncoder.applyTo(
final int produced = patch.applyTo(
word,
patch,
trie.traversalDirection(),
output,
0,
output.length);
if (produced != PatchCommandEncoder.APPLY_INSUFFICIENT_CAPACITY) {
if (produced != CompiledPatchCommand.APPLY_INSUFFICIENT_CAPACITY) {
final String stem = new String(output, 0, produced);
}
```
@@ -90,12 +89,14 @@ For multiple candidates:
```java
final String word = "axes";
for (final String patch : trie.getAll(word)) {
final String stem = PatchCommandEncoder.apply(word, patch);
for (final CompiledPatchCommand patch : trie.getAll(word)) {
final String stem = patch.apply(word);
System.out.println(word + " -> " + stem + " (" + patch + ")");
}
```
The historical `PatchCommandEncoder.apply(...)` API still exists for compatibility with code that directly handles serialized patch-command strings, but it is deprecated because it reparses the command on every call. See [Migration and Backward Compatibility](migration-and-backward-compatibility.md) for the old and new forms side by side.
## Understand reduction modes
Reduction mode determines how mutable subtrees are merged during compilation. All modes operate on full subtree semantics rather than only on local node content.

View File

@@ -1,75 +1,133 @@
# Programmatic Usage
This document provides the programmatic entry point to **Radixor**.
Radixor code and model data are separate runtime components. Every example on this page requires `org.egothor:radixor:<radixor-version>` as an `implementation` dependency and at least one model JAR as a runtime dependency. The core JAR contains no `stemmer.gz`.
Radixor follows a clear lifecycle:
For complete dependency patterns, lifecycle guidance, and troubleshooting, use [Model Selection and Loading](model-selection-and-loading.md). The generated [model catalog](stemmer-model-catalog.md) records the current artifacts, versions, checksums, and provenance.
1. acquire a compiled stemmer,
2. query it for patch commands,
3. apply those commands to produce stems,
4. reopen and extend the compiled structure when needed.
## 1. Minimal use: the Polish default
## Conceptual model
Dependency prerequisite:
Radixor is dictionary-driven, but runtime stemming does not operate by scanning raw dictionary files. A source dictionary is parsed as a sequence of canonical stems and their known variants. Each variant is converted into a compact patch command that transforms the variant into the stem, while the stem itself may optionally be stored as a canonical no-op patch. The mutable trie is then reduced into a compiled read-only structure that stores ordered values and their counts at addressed nodes.
Two consequences matter for developers:
- the quality and coverage of stemming behavior depend on dictionary richness,
- runtime usage is based on compiled patch-command lookup rather than on direct dictionary traversal.
This is why Radixor can generalize beyond explicitly listed forms and why compiled artifacts are well suited for deployment.
## Documentation map
The programmatic API is easier to understand when split by developer task:
- [Loading and Building Stemmers](programmatic-loading-and-building.md) explains how to acquire a compiled stemmer from bundled resources, textual dictionaries, binary artifacts, or direct builder usage.
- [Lookup Edge Optimization](lookup-edge-optimization.md) explains dense child lookup tuning and the speed/memory trade-off when materializing compiled tries.
- [Querying and Ambiguity Handling](programmatic-querying-and-ambiguity.md) explains `get(...)`, `getAll(...)`, `getEntries(...)`, patch application, and the practical meaning of reduction modes.
- [Extending and Persisting Compiled Tries](programmatic-extending-and-persistence.md) explains how to reopen compiled tries, add new lexical data, rebuild them, and store them as binary artifacts.
## Core types
The main types involved in programmatic usage are:
- `FrequencyTrie.Builder<V>` for mutable construction and extension,
- `FrequencyTrie<V>` for the compiled read-only trie,
- `PatchCommandEncoder` for creating and applying patch commands,
- `StemmerPatchTrieLoader` for loading bundled or textual dictionaries,
- `StemmerPatchTrieBinaryIO` for reading and writing compressed binary artifacts,
- `FrequencyTrieBuilders` for reconstructing a mutable builder from a compiled trie,
- `ReductionMode` and `ReductionSettings` for controlling compilation semantics.
## Java module system (JPMS)
The core artifact is published as an explicit JPMS module:
```java
module org.egothor.radixor;
```groovy
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-model-pl-pl-unimorph:1.0.0'
```
A named consuming module uses:
```java
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.PL_PL,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final String word = "koty";
final CompiledPatchCommand patch = trie.get(word);
final String stem = patch == null ? word : patch.apply(word);
```
`Language.PL_PL` resolves to `pl-pl-unimorph`. The loader creates the registry internally through the thread context class loader.
## 2. Explicit model selection
Dependency prerequisite: replace or supplement the default dependency with `runtimeOnly 'org.egothor:radixor-model-pl-pl-polimorf:1.0.0'`.
```java
module example.consumer {
requires org.egothor.radixor;
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor polimorf = registry.require("pl-pl-polimorf");
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadCompiled(
polimorf,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
The stable model-ID overload performs the same exact selection without a separately retained registry:
```java
final FrequencyTrie<CompiledPatchCommand> trie =
StemmerPatchTrieLoader.loadCompiled(
"pl-pl-polimorf",
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
```
## 3. Multiple variants for one language
Dependency prerequisite: both `radixor-model-pl-pl-unimorph:1.0.0` and `radixor-model-pl-pl-polimorf:1.0.0` at runtime.
```java
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
final StemmerModelDescriptor unimorph = registry.require("pl-pl-unimorph");
final StemmerModelDescriptor polimorf = registry.require("pl-pl-polimorf");
final FrequencyTrie<CompiledPatchCommand> unimorphTrie =
StemmerPatchTrieLoader.loadCompiled(unimorph, true, reductionMode);
final FrequencyTrie<CompiledPatchCommand> polimorfTrie =
StemmerPatchTrieLoader.loadCompiled(polimorf, true, reductionMode);
final StemmerModelDescriptor defaultPolish =
registry.requireDefault(StemmerPatchTrieLoader.Language.PL_PL);
if (!"pl-pl-unimorph".equals(defaultPolish.id())) {
throw new IllegalStateException(
"Unexpected default Polish model: " + defaultPolish.id());
}
```
The core module is standalone and can be consumed directly as a normal Java module.
The tries remain independent. Radixor does not merge models or infer an alternative default from classpath order.
## Recommended reading order
## 4. Discovery
For most developers, the best order is:
Dependency prerequisite: whichever model artifacts the application intends to discover.
1. [Loading and Building Stemmers](programmatic-loading-and-building.md)
2. [Querying and Ambiguity Handling](programmatic-querying-and-ambiguity.md)
3. [Extending and Persisting Compiled Tries](programmatic-extending-and-persistence.md)
```java
final StemmerModelRegistry registry = StemmerModelRegistry.fromContextClassLoader();
## Next steps
for (final StemmerModelDescriptor descriptor : registry.models()) {
System.out.printf("%s %s %s %s/%d%n",
descriptor.id(), descriptor.language(), descriptor.version(),
descriptor.format(), descriptor.formatVersion());
}
- [Quick Start](quick-start.md)
- [CLI compilation](cli-compilation.md)
- [Dictionary format](dictionary-format.md)
- [Architecture and reduction](architecture-and-reduction.md)
final java.util.List<StemmerModelDescriptor> polish =
registry.findByLanguage(StemmerPatchTrieLoader.Language.PL_PL);
```
Results use deterministic model-ID order. See [Built-in Languages](built-in-languages.md) for default interpretation and the generated [catalog](stemmer-model-catalog.md) for provenance.
## 5. Advanced ClassLoader selection
Dependency prerequisite: the model JAR must be visible to the selected loader.
```java
final ClassLoader applicationLoader = application.getClass().getClassLoader();
final StemmerModelRegistry isolatedRegistry =
StemmerModelRegistry.fromClassLoader(applicationLoader);
```
This form is useful for plugin containers, isolated application servers, and tests. It can discover a different set from the thread context loader. See [ClassLoader troubleshooting](model-selection-and-loading.md#troubleshooting).
## 6. Error handling
Dependency prerequisite: none beyond core; this example demonstrates an absent optional model.
```java
try {
StemmerModelRegistry.fromContextClassLoader().require("pl-pl-polimorf");
} catch (final StemmerModelNotFoundException exception) {
System.err.println(exception.getMessage());
}
```
Missing models never produce an empty trie or arbitrary fallback. Duplicate IDs, unsupported formats, malformed descriptors, missing resources, and checksum mismatches are also fatal. The full exception mapping and remediation table are in [Model Selection and Loading](model-selection-and-loading.md#error-handling).
## Continue into the trie API
- [Loading and Building Stemmers](programmatic-loading-and-building.md)
- [Querying and Ambiguity Handling](programmatic-querying-and-ambiguity.md)
- [Extending and Persisting Compiled Tries](programmatic-extending-and-persistence.md)
- [Architecture](architecture.md)

View File

@@ -2,8 +2,25 @@
This guide introduces the fastest practical path to using **Radixor**.
If you are new to Radixor and want the shortest possible path to a first working stem, start with
[Fast Track](fast-track.md). This Quick Start is a broader developer walkthrough: it introduces the
main loading options, query methods, artifact workflow, and metadata model. For model-ID selection and failures, use [Model Selection and Loading](model-selection-and-loading.md).
Radixor separates preparation from runtime usage. Source dictionaries are used to derive patch commands and reduce them into a compact read-only trie. Runtime stemming then operates on that compiled structure rather than on the original dictionary text. A richer dictionary usually improves the quality and coverage of inferred transformations, including transformations that are applicable to words not explicitly present in the source material. The reduction step also removes a large amount of redundant lexical information, which is why very large dictionaries can still produce compact runtime artifacts. These artifacts can be persisted and loaded directly when needed.
From version 4 onward, the core and models are explicit dependencies:
```groovy
dependencies {
implementation 'org.egothor:radixor:<radixor-version>'
runtimeOnly 'org.egothor:radixor-models-standard:<catalog-version>'
}
```
The core JAR contains no dictionary. Replace the standard pack with `runtimeOnly 'org.egothor:radixor-model-us-uk-default:1.0.0'` for the minimal English example below. For Polish, `Language.PL_PL` resolves `pl-pl-unimorph`; installing optional `pl-pl-polimorf` does not select it automatically.
Explicit PoliMorf loading uses `StemmerPatchTrieLoader.loadCompiled("pl-pl-polimorf", true, reductionMode)`. Its complete dictionary is supported, but construction is exceptional enough that repository verification runs it separately with a 6 GiB maximum heap. See [Model Selection and Loading](model-selection-and-loading.md#load-polimorf-explicitly) for the complete dependency and Java example.
A practical workflow usually consists of two independent phases:
1. obtain a compiled stemmer,
@@ -13,25 +30,26 @@ A practical workflow usually consists of two independent phases:
A compiled stemmer can be obtained in three common ways.
### Use a bundled language dictionary
### Use an external language model
Radixor ships with bundled dictionaries for a set of supported languages. These resources are line-oriented dictionaries stored with the library and compiled into a `FrequencyTrie<String>` when loaded. The loader can also store the canonical stem itself as a no-op patch command. Compiled trie artifacts now persist self-describing metadata, including the traversal direction and compilation reduction settings used to build the artifact.
Language dictionaries are independently versioned model JARs discovered by `StemmerModelRegistry`. The root `org.egothor:radixor` JAR contains no dictionary bytes. The loader compiles a selected model into a `FrequencyTrie<CompiledPatchCommand>`; compiled trie artifacts retain self-describing traversal and reduction metadata.
```java
import java.io.IOException;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
public final class BundledStemmerExample {
public final class RegisteredModelExample {
private BundledStemmerExample() {
private RegisteredModelExample() {
throw new AssertionError("No instances.");
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
@@ -49,6 +67,7 @@ Compiled stemmers can be stored as GZip-compressed binary artifacts and loaded d
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
@@ -59,7 +78,7 @@ public final class LoadBinaryStemmerExample {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"));
System.out.println("Canonical node count: " + trie.size());
@@ -73,6 +92,7 @@ You can tune in-memory child lookup density at load time without changing the ar
import java.io.IOException;
import java.nio.file.Path;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.StemmerPatchTrieLoader;
@@ -83,10 +103,10 @@ public final class LoadBinaryStemmerExampleTuned {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> fast = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> fast = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"),
1024);
final FrequencyTrie<String> compact = StemmerPatchTrieLoader.loadBinary(
final FrequencyTrie<CompiledPatchCommand> compact = StemmerPatchTrieLoader.loadBinaryCompiled(
Path.of("stemmers", "english.radixor.gz"),
128);
@@ -107,7 +127,7 @@ A dedicated CLI compilation workflow deserves its own focused page and should re
## 2. Use the compiled stemmer
A compiled `FrequencyTrie<String>` stores patch commands, not final stems. Querying therefore has two steps:
A compiled `FrequencyTrie<CompiledPatchCommand>` stores patch commands, not final stems. Querying therefore has two steps:
1. retrieve one or more patch commands from the trie,
2. apply each patch command to the original input word.
@@ -121,8 +141,8 @@ Use `get(...)` when the application needs a single preferred transformation.
```java
import java.io.IOException;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.FrequencyTrie;
import org.egothor.stemmer.PatchCommandEncoder;
import org.egothor.stemmer.ReductionMode;
import org.egothor.stemmer.StemmerPatchTrieLoader;
@@ -133,14 +153,14 @@ public final class SingleStemExample {
}
public static void main(final String[] arguments) throws IOException {
final FrequencyTrie<String> trie = StemmerPatchTrieLoader.load(
final FrequencyTrie<CompiledPatchCommand> trie = StemmerPatchTrieLoader.loadCompiled(
StemmerPatchTrieLoader.Language.US_UK,
true,
ReductionMode.MERGE_SUBTREES_WITH_EQUIVALENT_RANKED_GET_ALL_RESULTS);
final String word = "running";
final String patch = trie.get(word);
final String stem = PatchCommandEncoder.apply(word, patch);
final CompiledPatchCommand patch = trie.get(word);
final String stem = patch == null ? word : patch.apply(word);
System.out.println(word + " -> " + stem + " (" + patch + ")");
}
@@ -153,10 +173,10 @@ Use `getAll(...)` when the application should preserve ambiguity instead of coll
```java
final String word = "axes";
final String[] patches = trie.getAll(word);
final CompiledPatchCommand[] patches = trie.getAll(word);
for (final String patch : patches) {
final String stem = PatchCommandEncoder.apply(word, patch);
for (final CompiledPatchCommand patch : patches) {
final String stem = patch.apply(word);
System.out.println(word + " -> " + stem + " (" + patch + ")");
}
```
@@ -168,11 +188,12 @@ For diagnostics or advanced ranking logic, use `getEntries(...)` to obtain value
```java
import java.util.List;
import org.egothor.stemmer.CompiledPatchCommand;
import org.egothor.stemmer.ValueCount;
final List<ValueCount<String>> entries = trie.getEntries("axes");
final List<ValueCount<CompiledPatchCommand>> entries = trie.getEntries("axes");
for (final ValueCount<String> entry : entries) {
for (final ValueCount<CompiledPatchCommand> entry : entries) {
System.out.println(entry.value() + " -> " + entry.count());
}
```
@@ -210,7 +231,11 @@ public final class ExtendCompiledStemmerExample {
String[]::new,
settings);
builder.put("microservices", "Na");
final PatchCommandEncoder encoder = PatchCommandEncoder.builder()
.traversalDirection(compiledTrie.traversalDirection())
.build();
builder.put("microservices", encoder.encode("microservices", "microservice"));
final FrequencyTrie<String> updatedTrie = builder.build();
@@ -239,3 +264,5 @@ Dictionary compilation is usually a one-time preparation step and is generally f
Every compiled trie artifact stores a `TrieMetadata` descriptor together with the immutable trie payload. That metadata currently records the binary format version, the `WordTraversalDirection`, the `ReductionSettings` used during compilation, the declared `DiacriticProcessingMode`, and the selected `CaseProcessingMode`. Traversal, case processing, and diacritic processing are applied during runtime lookup (`get`, `getAll`), and case/diacritic processing are also applied during dictionary insertion when a trie is built.
`DiacriticProcessingMode.AS_IS` keeps dictionary keys and lookup keys unchanged. `DiacriticProcessingMode.REMOVE` strips diacritics from dictionary keys and lookup keys (for Czech diacritics and broad European Latin-script variants). `DiacriticProcessingMode.AS_IS_AND_STRIPPED_FALLBACK` is currently not supported and raises an `UnsupportedOperationException`.
!!! note "Radixor 4 model artifacts"
Language dictionaries are independently versioned runtime model artifacts, not resources embedded in `radixor`. Language-based APIs resolve deterministic defaults through `StemmerModelRegistry`; see [Stemmer Models](stemmer-models.md).

View File

@@ -103,6 +103,24 @@ This turns reduction into a canonicalization process:
That is how Radixor eliminates duplicated equivalent subtrees.
## Uniform-subtree contraction
Radixor performs one additional internal reduction before each public reduction mode is applied.
When all reachable entries below a subtree have the same preferred patch command, the subtree is
contracted into an accepting leaf for that command.
This optimization is deliberately narrower than the public reduction modes:
- it is based on preferred `get()` behavior,
- it does not depend on child edge shape once the preferred command is uniform,
- it removes lookup depth that cannot affect the selected command,
- it preserves the standard single-result stemming path used by `StemmerPatchTrieLoader.loadCompiled(...)`.
The effect is especially visible in large dictionary tries with many inflected forms that map to
the same command class, such as no-op preservation or common suffix deletion. Runtime lookup can
return the accepting leaf as soon as it is reached instead of traversing the remaining characters
only to discover the same command deeper in the trie.
## Count aggregation and compiled state
When multiple original build-time subtrees collapse into one canonical reduced node, local counts may be aggregated.

View File

@@ -2,6 +2,8 @@
Radixor publishes durable build outputs to GitHub Pages from qualifying runs of `.github/workflows/pages.yml`.
The workflow builds maintained MkDocs documentation and the generated model catalog from the staged source tree under `build/mkdocs-source/`. It then merges the rendered site into the separate `gh-pages` publication worktree while preserving `builds/`. The main branch stores neither generated Markdown nor rendered site output. The publication retains the ten newest numbered report sets and maintains `builds/latest/` as a stable alias.
This page is the central entry point for published project artifacts, including build summaries, API documentation, test and quality reports, benchmark outputs, and software composition materials. It is intended both for routine project inspection and for linking stable report surfaces from external references such as the README, release notes, or development workflows.
## Stable entry points
@@ -42,15 +44,14 @@ They are useful for dependency inspection, downstream integration, compliance-or
## Benchmark outputs and badge metadata
These resources expose benchmark results and generated badge metadata derived from the latest published build:
These resources expose benchmark results and generated badge metadata derived from the latest published build. JMH benchmark reports are published as TXT and CSV files; the historical Porter comparison badge is no longer generated.
- [JMH benchmark results (TXT)](https://leogalambos.github.io/Radixor/builds/latest/jmh/jmh-results.txt)
- [JMH benchmark results (CSV)](https://leogalambos.github.io/Radixor/builds/latest/jmh/jmh-results.csv)
- [Coverage badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/coverage-badge.json)
- [Mutation badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/pitest-badge.json)
- [Benchmark badge metadata](https://leogalambos.github.io/Radixor/builds/latest/metrics/jmh-badge.json)
The benchmark outputs provide direct access to the published JMH result files, while the badge metadata endpoints are intended for status surfaces such as the project README or other generated dashboards.
The benchmark outputs provide direct access to the published JMH result files. Coverage and mutation badge metadata endpoints are intended for status surfaces such as the project README or other generated dashboards.
## Practical usage

192
docs/stemmer-models.md Normal file
View File

@@ -0,0 +1,192 @@
# Stemmer Models
This page defines the model artifact and its maintenance lifecycle. Application developers should begin with [Model Selection and Loading](model-selection-and-loading.md); the generated [model catalog](stemmer-model-catalog.md) is the detailed inventory.
## Terminology
| Term | Definition |
|---|---|
| Radixor core | Java parsing, patch-command, trie, registry, descriptor, and loader code in `org.egothor:radixor` |
| Language | Locale-level identity such as `PL_PL`; not a dictionary or model |
| Model ID | Stable identity of one concrete model, such as `pl-pl-unimorph` |
| Model artifact | Independently versioned JAR containing one descriptor, one runtime dictionary, and licensing material |
| Source dictionary | Upstream lexical or morphological source recorded in provenance |
| Runtime dictionary | GZip-compressed UTF-8 Radixor tab-separated data consumed during trie construction |
| Compiled trie | In-memory lookup structure built by the loader; not the `stemmer.gz` resource |
| Default model | Stable ID selected by a language-oriented loader call |
| Optional model | Discoverable only when installed and selected explicitly; PoliMorf is optional for Polish |
Core version, model artifact version, catalog version, source dictionary version, and model format version are separate compatibility axes. Updating Java code need not republish unchanged model bytes; updating one model need not release core or every other model.
## Model artifact identity and layout
A module named `models/<model-id>` publishes:
```text
org.egothor:radixor-model-<model-id>:<model-version>
```
The built PoliMorf JAR has this effective tree:
```text
META-INF/
LICENSES/PoliMorf-BSD-2-Clause.txt
MANIFEST.MF
radixor/
models.index
models/pl-pl-polimorf.properties
org/egothor/stemmer/models/pl-pl-polimorf/stemmer.gz
```
Each UniMorph-derived model instead contains one model-specific
`META-INF/NOTICE/<model-id>-data.txt`. That notice records the upstream attribution, the Radixor
transformations and contribution statement, the ShareAlike distribution terms, and the canonical
CC BY-SA 3.0 URI. The repository has no root CC license directory because CC BY-SA applies to
these model-data artifacts, not to the BSD-3-Clause Radixor Java software. PoliMorf retains only
its BSD-2-Clause data license.
`models.index` contains the descriptor path. The descriptor contains the exact resource path. No Java provider class is required, and model modules do not compile against a core API.
## Discovery and integrity
`StemmerModelRegistry` asks the selected `ClassLoader` for every `META-INF/radixor/models.index`. It sorts index URLs, validates each non-comment entry, loads the named descriptors, sorts descriptors by model ID, and rejects duplicate IDs. It does not scan arbitrary JAR entries.
Descriptor parsing verifies:
- the model-ID syntax;
- required nonblank runtime properties;
- a known `Language` enum name;
- format `radixor-dictionary-tsv-gzip` and format version `1`;
- the exact namespaced resource path;
- presence of the runtime resource;
- a lowercase 64-character SHA-256 value.
Loading then reads the compressed resource bytes through the descriptor's discovering class loader, compares their SHA-256 digest, opens GZip, parses UTF-8 Radixor dictionary rows, and constructs a trie. Duplicate-ID and checksum checks make selection independent of classpath order.
## Descriptor fields
The convention plugin generates these fields:
| Property | Role | Meaning |
|---|---|---|
| `model.id` | Authoritative runtime identity | Stable model ID |
| `model.version` | Authoritative artifact identity | Independently managed model version |
| `model.language` | Authoritative selection metadata | Existing `Language` enum value |
| `model.displayName` | Display metadata | Human-readable name |
| `model.resource` | Authoritative loading metadata | Namespaced GZip resource |
| `model.default` | Catalog/build declaration | Whether the module declares itself a default; runtime language selection uses `Language.defaultModelId()` |
| `model.format` | Authoritative compatibility metadata | `radixor-dictionary-tsv-gzip` |
| `model.formatVersion` | Authoritative compatibility metadata | Currently `1` |
| `model.sha256` | Authoritative integrity metadata | Digest of the compressed source bytes |
| `model.rightToLeft` | Processing metadata | Language direction recorded by the build |
| `model.caseProcessing` | Processing metadata | `LOWERCASE_WITH_LOCALE_ROOT` |
| `model.diacriticProcessing` | Processing metadata | `AS_IS` |
| `model.storeOriginal` | Processing metadata | Currently `true` |
| `source.name` | Provenance | Source dictionary name |
| `source.version` | Provenance | Upstream version or the legacy-import sentinel |
| `source.project` | Provenance | Upstream project |
| `source.repository` | Provenance | Official language repository |
| `source.dataset` | Provenance | Upstream dataset and lexical-source identity |
| `source.revision` | Provenance | Exact revision or `not-recorded-in-legacy-import` |
| `source.revisionStatus` | Provenance | `recorded` or `not-recorded-in-legacy-import` |
| `source.license` | Provenance | SPDX or project license reference |
| `source.licenseUri` | Provenance | Canonical license URI |
| `source.attribution` | Provenance | Attribution supplied by the official source |
| `source.verificationDate` | Provenance | Date the maintained upstream information was checked |
| `transformations.summary` | Provenance | Material Radixor conversion operations |
| `compiler.radixorVersion` | Provenance | Compiler lineage recorded by the plugin |
| `compiler.radixorCommit` | Provenance | Commit when available; currently `unavailable` |
| `statistics.groups` | Provenance/statistics | Currently `unavailable` |
| `statistics.forms` | Provenance/statistics | Currently `unavailable` |
The current registry consumes the authoritative `model.*` identity, format, resource, and checksum fields. Processing and provenance fields remain packaged for audit and catalog generation but are not all exposed as typed `StemmerModelDescriptor` accessors. The generated catalog is the supported documentation view of source name, version, license, checksum, and size.
## Immutable input to runtime model
The packaging sequence is:
```text
models/<id>/src/modelInput/stemmer.gz
-> validate GZip, strict UTF-8, rows, metadata, version, and license
-> copy identical bytes into build/generated/modelResources
-> generate descriptor, index, and packaged license
-> package radixor-model-<id>-<version>.jar
-> discover from the application's runtime classpath
-> verify checksum, parse dictionary, and build a trie
```
Application runtime never reads `src/modelInput` from a source checkout.
For PoliMorf, the immutable input is exactly:
`models/pl-pl-polimorf/src/modelInput/stemmer.gz`
Its required upstream license is:
`models/pl-pl-polimorf/src/modelInput/LICENSE-BSD-2-Clause.txt`
The final runtime resource is exactly:
`org/egothor/stemmer/models/pl-pl-polimorf/stemmer.gz`
## Aggregate projects
| Project | Published coordinate | Contents and purpose |
|---|---|---|
| `models/standard` | `org.egothor:radixor-models-standard:<catalog-version>` | POM-only aggregate with one transitive runtime default per language; excludes PoliMorf |
| `models/bom` | `org.egothor:radixor-models-bom:<catalog-version>` | POM-only Maven dependency-management constraints for all individual published model versions |
Neither catalog artifact publishes a binary, sources, or Javadoc JAR. The standard aggregate resolves model JARs because its POM contains runtime dependencies. Importing the BOM only manages versions and resolves no model by itself. JMH, tests, and quality evaluation depend directly on individual model projects through non-production Gradle configurations.
The Maven dependency BOM is not a software bill of materials. The root `cyclonedxDirectBom` task generates the project-wide CycloneDX SBOM under `build/reports/sbom/`; it does not write into `models/bom/build/`.
`models/build/` is an ignored Gradle output directory for the implicit lifecycle parent `:models`, not a source module. CycloneDX direct tasks exposed on subprojects by the root plugin are disabled, so the supported build does not write an SBOM there. Aggregate model reports are owned by the root project under `build/reports/models/`; individual model reports and publication files stay under `models/<model-id>/build/`.
## Create or update a model module
1. Choose a stable lowercase model ID matching the module directory.
2. Add `models/<id>/model-version.txt`; do not derive it from core.
3. Apply `org.egothor.radixor.model` in the module build script.
4. Declare `modelId`, `language`, `displayName`, `defaultModel`, repository, dataset, revision and status, license URI, attribution, verification date, and transformations.
5. Put immutable `stemmer.gz` and a model-specific `NOTICE-model-data.txt` under `src/modelInput/`. The notice must identify the applicable data license and canonical URI, upstream attribution, transformations, and derived-data contributions without implying that the core software uses that license.
6. Add the module ID and its `default` or `optional` build-topology role to `models/model-projects.properties`. `settings.gradle`, verification, standard membership, BOM constraints, tests, and JMH all consume that list; descriptor metadata remains authoritative for model identity and language properties.
7. Run:
```bash
./gradlew --no-daemon :models:<model-id>:validateModelInput
./gradlew --no-daemon :models:<model-id>:prepareModelResources
./gradlew --no-daemon :models:<model-id>:verifyModelDescriptor
./gradlew --no-daemon :models:<model-id>:verifyModelJar
./gradlew --no-daemon :models:<model-id>:check
./gradlew --no-daemon runtimeModelIntegrationTest -PmodelId=<model-id>
```
Validation fails for missing inputs, notices, attribution, repository, revision status, Radixor contribution and transformation disclosures, ShareAlike and no-endorsement statements, notice byte identity, unsafe or mismatched ID, invalid semantic version, invalid GZip/UTF-8, invalid dictionary rows, checksum mismatch, wrong packaged path, duplicate dictionaries, or dictionaries in sources/Javadoc artifacts. The explicit legacy revision sentinel is valid; an absent revision or status is not. The PoliMorf module separately validates its complete BSD-2-Clause license and attribution.
Copying an arbitrary `stemmer.gz` into an application is insufficient: registry discovery requires an index, a valid descriptor, namespaced resource, checksum, version, language, format declaration, and licensing material.
## Release boundaries
| Tag | Publishes | Does not publish |
|---|---|---|
| `release@<core-version>` | Root `org.egothor:radixor` software artifacts | Model JARs, standard pack, or BOM |
| `model/<model-id>@<model-version>` | Exactly the matching independently versioned model | Core, other models, standard pack, BOM, JMH, or full quality suite |
| `models-catalog@<catalog-version>` | Standard aggregate and models BOM | Individual model JARs or core |
Local validation for PoliMorf 1.0.0 is:
```bash
./tools/parse-model-release-tag.sh "model/pl-pl-polimorf@1.0.0" .
./gradlew --no-daemon :models:pl-pl-polimorf:check
./gradlew --no-daemon runtimeModelIntegrationTest -PmodelId=pl-pl-polimorf
./gradlew --no-daemon :models:pl-pl-polimorf:validateModelRelease \
-PmodelReleaseVersion=1.0.0
./gradlew --no-daemon :models:pl-pl-polimorf:packageModelReleaseCandidate \
-PmodelReleaseVersion=1.0.0
```
`runtimeModelIntegrationTest` uses an isolated JVM, defaults to a 6 GiB maximum heap, and can be overridden with `-PradixorLargeModelMaxHeap=10g`. For PoliMorf, `validateModelRelease` depends on this complete runtime construction and real stemming smoke verification in addition to descriptor, checksum, license, and package validation. The generic release workflow still selects and publishes only the requested model. The commands above are local validation only; repository owners control tags and publication.
## Documentation and troubleshooting
`prepareMkDocsSource` generates the catalog only at `build/mkdocs-source/stemmer-model-catalog.md`; generated Markdown and rendered site content are not tracked. For runtime failures, dependency inspection, ClassLoader isolation, and fat-JAR guidance, see [Model Selection and Loading](model-selection-and-loading.md#troubleshooting).

87
docs/stemming-quality.md Normal file
View File

@@ -0,0 +1,87 @@
# Stemming quality evaluation
The explicit `stemmingQuality` analysis measures agreement between stemmer outputs and gold-standard equivalence classes represented by registered multilingual model dictionary rows. Dictionary text remains unchanged; reports and diagnostics use English.
JMH adapters, registries, third-party versions, language mappings, and preparation remain in `src/jmh`. The evaluator, reports, audits, and tests reside in the standard `src/test` source set. The former `src/stemmingQualityTest` source set was removed, and neither analytical nor JMH classes enter the production JAR.
## Language and adapter coverage
The authoritative Radixor universe is the validated one-to-one reconciliation of every `StemmerPatchTrieLoader.Language` value with its registered default model descriptor. All 20 current values have exactly one documented default. Optional comparison models, including `pl-pl-polimorf`, are identified separately and never replace default benchmark rows. Third-party combinations come only from explicit JMH adapter metadata.
Default Polish evaluation is therefore `Radixor` with model `pl-pl-unimorph`. A future PoliMorf evaluation is a distinct `Radixor` / `pl-pl-polimorf` row. Evaluation classpaths receive individual models through direct non-production Gradle dependencies; ordinary applications inherit none of them from the core.
Complete PoliMorf trie construction and deterministic stemming smoke fixtures are runtime-verified separately. That functional verification is not a linguistic-quality measurement and does not justify rewriting the historical quality snapshot.
The expected matrix is constructed before evaluation from stemmer, language, dictionary mode, and supported output policy. Generation fails on missing, duplicate, unexpected, or stale keys.
## Dictionary groups and modes
Every usable parsed row is one gold-standard group. Exact duplicate strings are removed only within that row; identical forms in different rows remain distinct. `ALL_WORDS` preserves every valid form. `LOWERCASE_GROUPS_ONLY` excludes a complete group containing an uppercase or titlecase Unicode code point. Retained words are not lowercased or normalized by the evaluator.
## Output policies
`PRIMARY_OUTPUT` uses the deterministic JMH output and defines a strict partition.
For multi-output adapters, `C(w)` is the immutable, sorted, exactly deduplicated candidate set. It is non-null, non-empty, contains no null, and contains the primary output. Radixor obtains alternatives through `getAll`. The repository's Morphologik lookups can return distinct lemma strings and are multi-output. Configured Hunspell filters can emit several stems at one token position. Other adapters emit only primary rows.
`ANY_CANDIDATE` is an optimistic oracle-assisted pairwise upper bound. A same-group pair succeeds when its sets intersect. A cross-group pair is an error only when both sets are the same singleton; otherwise unequal candidates can be selected for that pair. Choices may vary between pairs and need not form one realizable global assignment.
`ALL_CANDIDATES` activates every candidate. Two forms are related when their sets intersect, for both same-group and cross-group pairs. This relation can overlap and need not be transitive. A pair sharing several candidates is counted once.
The evaluator verifies:
```text
ANY under <= PRIMARY under
ALL under <= PRIMARY under
ANY under = ALL under
ANY over <= PRIMARY over
ALL over >= PRIMARY over
```
## Pair definitions and efficient counting
For `C2(n) = n(n-1)/2`:
```text
underPossible = sum_g C2(n_g)
overPossible = C2(N) - sum_g C2(n_g)
```
Under-stemming counts unrelated same-group pairs. Over-stemming counts related cross-group pairs. Primary output uses global and per-group stem frequencies. Candidate sets are canonical signatures counted globally and per group. An inverted candidate-to-signature index discovers intersections, and signature pairs shared through several candidates are deduplicated. `ANY_CANDIDATE` over-stemming uses only equal singleton signatures. All pair arithmetic uses checked `long` operations; complete production word pairs are never enumerated.
## Confusion and aggregate metrics
```text
TP = underPossible - underError
FN = underError
FP = overError
TN = overPossible - overError
```
Under-stemming is `FN/(TP+FN)` and over-stemming is `FP/(TN+FP)`; their denominators differ. The CSV also publishes precision, recall, specificity, accuracy, balanced accuracy, F0.5, F1, F2, Jaccard, Fowlkes-Mallows, Matthews correlation coefficient, and pairwise error rate. F0.5 emphasizes precision and over-stemming, F1 balances precision and recall, and F2 emphasizes recall and under-stemming. Accuracy and error rate can be dominated by the large cross-group true-negative population. Metrics use raw counts, not rounded rates. Zero denominators produce `n/a` in Markdown and empty CSV fields.
Only `PRIMARY_OUTPUT` receives partition metrics: Adjusted Rand Index, homogeneity, completeness, V-measure, and normalized mutual information with arithmetic-mean entropy normalization. Candidate policies remain inapplicable rather than being forced into artificial partitions.
Micro summaries sum confusion counts before calculation. Macro summaries average defined language values and retain coverage counts. Common-language comparisons use the exact language intersection and never score unsupported languages as zero. Rankings are separated by policy and metric; the default F0.5 choice is navigation, not a universal scientific preference.
Pearson and average-tie-rank Spearman reports use unrounded values and separate dictionary-mode and output-policy cohorts. Fewer than three observations, undefined inputs, and zero variance produce documented missing values. The reports provide reproducible data and make no automatic scientific conclusion.
## Exact accuracy and pairwise under-stemming
Exact textual accuracy and pairwise grouping use different denominators. One erroneous form in a 12-form group creates 11 erroneous pairs: with 88 singleton groups, exact accuracy can be 99% while pairwise under-stemming is `11/C2(12) = 16.666667%`. Singleton groups affect word accuracy but add no within-group pairs.
## Running the analysis
```bash
./gradlew stemmingQuality
./gradlew stemmingQuality -PstemmingQualityStemmer=Radixor -PstemmingQualityLanguage=DE_DE -PstemmingQualityMode=ALL_WORDS -PstemmingQualityAudit=true
```
Optional properties are `stemmingQualityLanguage`, `stemmingQualityStemmer`, `stemmingQualityMode`, `stemmingQualityOutputPolicy`, `stemmingQualityRankMetric`, `stemmingQualityAudit`, and `stemmingQualityAuditLimit`. Policies are `PRIMARY_OUTPUT`, `ANY_CANDIDATE`, and `ALL_CANDIDATES`. Filtered reports carry `-filtered` and cannot overwrite complete output.
Generated files under `build/reports/stemming-quality/` include `stemming-quality.md`, `stemming-quality.csv`, `metric-correlations-pearson.csv`, `metric-correlations-spearman.csv`, and optional audit Markdown.
## Limitations
These measurements evaluate agreement with the available dictionary grouping. They do not capture every semantic, morphological, downstream, or dataset-specific property. `ANY_CANDIDATE` is optimistic and may not be globally realizable. `ALL_CANDIDATES` measures an overlap graph rather than a partition. Language coverage must remain visible in cross-stemmer comparisons. No single published metric establishes universal superiority; multiple metrics and their correlations are provided for transparent scientific assessment.
Historical checked-in quality results retain their original inputs and claims. The optional PoliMorf model is not attributed to snapshots that predate it. See [Model Selection and Loading](model-selection-and-loading.md) and the generated [model catalog](stemmer-model-catalog.md).

View File

@@ -145,6 +145,14 @@ defensive fallback in case of future tag drift.
All examples use Gradle with JUnit Platform integration:
- Default fast test run:
```
./gradlew test
```
The default `test` task excludes `slow` tests. Supplying `-DincludeTags` or `-PincludeTags` still excludes `slow` unless the include expression contains `slow`; supplying an explicit exclude expression replaces the default. Long-running bundled-dictionary compilation and full-language loading checks therefore run only through an explicit tag expression such as `-DincludeTags=slow` or a dedicated profile such as `ciSlow`.
- Only unit tests:
```
@@ -154,7 +162,7 @@ All examples use Gradle with JUnit Platform integration:
- Integration tests only:
```
./gradlew test -DincludeTags=integration
./gradlew test -DincludeTags=integration -DexcludeTags=slow
```
- Only trie subsystem tests:

View File

@@ -5,15 +5,15 @@ antlr:antlr:2.7.7=pitest
com.github.oowekyala.ooxml:nice-xml-messages:3.1=pmd
com.google.code.gson:gson:2.13.2=pmd
com.google.errorprone:error_prone_annotations:2.41.0=pmd
net.bytebuddy:byte-buddy-agent:1.17.7=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.bytebuddy:byte-buddy:1.17.7=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-api:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-engine:1.9.3=jmhRuntimeClasspath,testRuntimeClasspath
net.jqwik:jqwik-time:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-web:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik:1.9.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
net.bytebuddy:byte-buddy-agent:1.17.7=testCompileClasspath,testRuntimeClasspath
net.bytebuddy:byte-buddy:1.17.7=testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-api:1.9.3=testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-engine:1.9.3=testRuntimeClasspath
net.jqwik:jqwik-time:1.9.3=testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik-web:1.9.3=testCompileClasspath,testRuntimeClasspath
net.jqwik:jqwik:1.9.3=testCompileClasspath,testRuntimeClasspath
net.sf.jopt-simple:jopt-simple:4.9=pitest
net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath
net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
net.sf.saxon:Saxon-HE:12.9=pmd
net.sourceforge.pmd:pmd-ant:7.20.0=pmd
net.sourceforge.pmd:pmd-core:7.20.0=pmd
@@ -22,37 +22,45 @@ org.antlr:antlr4-runtime:4.9.3=pmd
org.antlr:stringtemplate:3.2.1=pitest
org.apache.commons:commons-lang3:3.18.0=pitest
org.apache.commons:commons-lang3:3.20.0=pmd
org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apache.commons:commons-text:1.14.0=pitest
org.apiguardian:apiguardian-api:1.1.2=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.apache.lucene:lucene-analysis-common:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apache.lucene:lucene-analysis-morfologik:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apache.lucene:lucene-analysis-stempel:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apache.lucene:lucene-core:10.5.0=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apache.opennlp:opennlp-tools:2.5.4=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.apiguardian:apiguardian-api:1.1.2=testCompileClasspath,testRuntimeClasspath
org.carrot2:morfologik-fsa:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.carrot2:morfologik-polish:2.1.9=jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.carrot2:morfologik-stemming:2.1.9=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.checkerframework:checker-qual:3.52.1=pmd
org.jacoco:org.jacoco.agent:0.8.14=jacocoAgent,jacocoAnt
org.jacoco:org.jacoco.ant:0.8.14=jacocoAnt
org.jacoco:org.jacoco.core:0.8.14=jacocoAnt
org.jacoco:org.jacoco.report:0.8.14=jacocoAnt
org.junit.jupiter:junit-jupiter-api:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter-engine:5.14.3=jmhRuntimeClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter-params:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.junit.platform:junit-platform-commons:1.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.junit.platform:junit-platform-engine:1.14.3=jmhRuntimeClasspath,testRuntimeClasspath
org.junit.platform:junit-platform-launcher:1.14.3=jmhRuntimeClasspath,testRuntimeClasspath
org.junit:junit-bom:5.14.3=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.mockito:mockito-core:5.23.0=jmhRuntimeClasspath,mockitoAgent,testCompileClasspath,testRuntimeClasspath
org.mockito:mockito-junit-jupiter:5.23.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.objenesis:objenesis:3.3=jmhRuntimeClasspath,testRuntimeClasspath
org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.openjdk.jmh:jmh-generator-asm:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.openjdk.jmh:jmh-generator-bytecode:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.openjdk.jmh:jmh-generator-reflection:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.opentest4j:opentest4j:1.3.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter-api:5.14.3=testCompileClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter-engine:5.14.3=testRuntimeClasspath
org.junit.jupiter:junit-jupiter-params:5.14.3=testCompileClasspath,testRuntimeClasspath
org.junit.jupiter:junit-jupiter:5.14.3=testCompileClasspath,testRuntimeClasspath
org.junit.platform:junit-platform-commons:1.14.3=testCompileClasspath,testRuntimeClasspath
org.junit.platform:junit-platform-engine:1.14.3=testRuntimeClasspath
org.junit.platform:junit-platform-launcher:1.14.3=testRuntimeClasspath
org.junit:junit-bom:5.14.3=testCompileClasspath,testRuntimeClasspath
org.mockito:mockito-core:5.23.0=mockitoAgent,testCompileClasspath,testRuntimeClasspath
org.mockito:mockito-junit-jupiter:5.23.0=testCompileClasspath,testRuntimeClasspath
org.objenesis:objenesis:3.3=testRuntimeClasspath
org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.openjdk.jmh:jmh-generator-asm:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.openjdk.jmh:jmh-generator-bytecode:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.openjdk.jmh:jmh-generator-reflection:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.opentest4j:opentest4j:1.3.0=testCompileClasspath,testRuntimeClasspath
org.ow2.asm:asm-analysis:9.9.1=pitest
org.ow2.asm:asm-commons:9.9=jacocoAnt
org.ow2.asm:asm-commons:9.9.1=pitest
org.ow2.asm:asm-tree:9.9=jacocoAnt
org.ow2.asm:asm-tree:9.9.1=pitest
org.ow2.asm:asm-util:9.9.1=pitest
org.ow2.asm:asm:9.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath
org.ow2.asm:asm:9.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.ow2.asm:asm:9.9=jacocoAnt
org.ow2.asm:asm:9.9.1=pitest,pmd
org.pcollections:pcollections:4.0.2=pmd
@@ -62,5 +70,7 @@ org.pitest:pitest-html-report:1.22.1=pitest
org.pitest:pitest-junit5-plugin:1.2.3=pitest
org.pitest:pitest:1.22.1=pitest
org.slf4j:jul-to-slf4j:1.7.36=pmd
org.slf4j:slf4j-api:2.0.17=jmhCompileClasspath,jmhRuntimeClasspath,stemmingQualityJmhRuntime
org.xmlresolver:xmlresolver:5.3.3=pmd
ua.net.nlp:morfologik-ukrainian-search:4.9.1=jmhRuntimeClasspath,stemmingQualityJmhRuntime
empty=annotationProcessor,compileClasspath,cyclonedxBom,jmhAnnotationProcessor,mainPmdAuxClasspath,runtimeClasspath,testAnnotationProcessor

View File

@@ -0,0 +1,69 @@
def cistemGoldStandardBaseUrl = 'https://raw.githubusercontent.com/LeonieWeissweiler/CISTEM/refs/heads/master/gold_standards'
def cistemGoldStandardFiles = [
'goldstandard1.txt',
'goldstandard2.txt'
]
def cistemGoldStandardDownloadDirectory = layout.buildDirectory.dir('third-party/cistem-gold-standards')
def cistemGoldStandardGeneratedResourcesDirectory = layout.buildDirectory.dir('generated/resources/cistem-gold-standards')
def cistemGoldStandardDownloadedFiles = cistemGoldStandardFiles.collect { String fileName ->
cistemGoldStandardDownloadDirectory.map { it.file(fileName) }
}
tasks.register('downloadCistemGoldStandards') {
group = 'build setup'
description = 'Downloads benchmark-only CISTEM German gold standards.'
outputs.files(cistemGoldStandardDownloadedFiles)
doLast {
cistemGoldStandardFiles.each { String fileName ->
final File targetFile = cistemGoldStandardDownloadDirectory.get().file(fileName).asFile
targetFile.parentFile.mkdirs()
if (!targetFile.exists()) {
final URL sourceUrl = new URL("${cistemGoldStandardBaseUrl}/${fileName}")
try {
sourceUrl.withInputStream { inputStream ->
targetFile.withOutputStream { outputStream ->
outputStream << inputStream
}
}
} catch (FileNotFoundException exception) {
throw new GradleException(
"Unable to download CISTEM gold standard ${fileName} from ${sourceUrl}.",
exception)
}
}
if (targetFile.length() <= 0L) {
throw new GradleException("Downloaded CISTEM gold standard ${fileName} was empty.")
}
}
}
}
tasks.register('prepareCistemGoldStandardResources', Copy) {
group = 'build setup'
description = 'Copies benchmark-only CISTEM German gold standards into the JMH resource output.'
dependsOn(tasks.named('downloadCistemGoldStandards'))
from(cistemGoldStandardDownloadDirectory) {
include 'goldstandard1.txt'
include 'goldstandard2.txt'
}
into(cistemGoldStandardGeneratedResourcesDirectory)
}
sourceSets {
jmh {
resources {
srcDir(cistemGoldStandardGeneratedResourcesDirectory)
}
}
}
tasks.named('processJmhResources') {
dependsOn(tasks.named('prepareCistemGoldStandardResources'))
}

View File

@@ -0,0 +1,121 @@
import org.gradle.plugins.ide.eclipse.model.SourceFolder
def hunspellDictionaryBaseUrl = 'https://raw.githubusercontent.com/wooorm/dictionaries/main/dictionaries'
def hunspellDictionaryLanguages = [
en: 'English',
cs: 'Czech',
de: 'German',
es: 'Spanish',
fr: 'French',
nl: 'Dutch',
pl: 'Polish',
uk: 'Ukrainian'
]
def hunspellDownloadDirectory = layout.buildDirectory.dir('third-party/hunspell')
def hunspellGeneratedResourcesDirectory = layout.buildDirectory.dir('generated/resources/hunspell')
def hunspellGeneratedResourcesPath = provider {
project.relativePath(hunspellGeneratedResourcesDirectory.get().asFile)
}
def hunspellEclipseClasspathAttributes = [
gradle_scope : 'jmh',
gradle_used_by_scope: 'jmh',
test : 'true'
]
def hunspellIsAbsolutePath = { String path ->
path.startsWith('/') || path ==~ /^[A-Za-z]:[\\\/].*/
}
def hunspellDownloadedFiles = hunspellDictionaryLanguages.keySet().collectMany { String code ->
[
hunspellDownloadDirectory.map { it.file("${code}/index.aff") },
hunspellDownloadDirectory.map { it.file("${code}/index.dic") },
hunspellDownloadDirectory.map { it.file("${code}/license") }
]
}
tasks.register('downloadHunspellBenchmarkDictionaries') {
group = 'build setup'
description = 'Downloads benchmark-only Hunspell dictionaries from wooorm/dictionaries.'
outputs.files(hunspellDownloadedFiles)
doLast {
hunspellDictionaryLanguages.each { String code, String displayName ->
['index.aff', 'index.dic', 'license'].each { String fileName ->
final File targetFile = hunspellDownloadDirectory.get().file("${code}/${fileName}").asFile
targetFile.parentFile.mkdirs()
if (!targetFile.exists()) {
final URL sourceUrl = new URL("${hunspellDictionaryBaseUrl}/${code}/${fileName}")
try {
sourceUrl.withInputStream { inputStream ->
targetFile.withOutputStream { outputStream ->
outputStream << inputStream
}
}
} catch (FileNotFoundException exception) {
throw new GradleException(
"Unable to download Hunspell ${fileName} file for ${displayName} (${code}) from ${sourceUrl}.",
exception)
}
}
if (targetFile.length() <= 0L) {
throw new GradleException("Downloaded Hunspell ${fileName} file for ${displayName} was empty.")
}
}
}
}
}
tasks.register('prepareHunspellBenchmarkResources', Copy) {
group = 'build setup'
description = 'Copies benchmark-only Hunspell dictionaries into the JMH resource output.'
dependsOn(tasks.named('downloadHunspellBenchmarkDictionaries'))
from(hunspellDownloadDirectory) {
include '**/index.aff'
include '**/index.dic'
include '**/license'
into 'hunspell'
}
into(hunspellGeneratedResourcesDirectory)
}
sourceSets {
jmh {
resources {
srcDir(hunspellGeneratedResourcesDirectory)
}
}
}
tasks.named('processJmhResources') {
dependsOn(tasks.named('prepareHunspellBenchmarkResources'))
}
eclipse {
classpath {
file {
whenMerged { classpath ->
String generatedPath = hunspellGeneratedResourcesPath.get()
classpath.entries.removeAll { entry ->
entry.hasProperty('path') && (
entry.path == generatedPath ||
hunspellIsAbsolutePath(entry.path)
)
}
SourceFolder hunspellEntry = new SourceFolder(generatedPath, null)
hunspellEntry.output = 'bin/jmh'
hunspellEclipseClasspathAttributes.each { String name, String value ->
hunspellEntry.entryAttributes[name] = value
}
classpath.entries.add(hunspellEntry)
}
}
}
}

View File

@@ -0,0 +1,30 @@
/*******************************************************************************
* Copyright (C) 2026, Leo Galambos
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
******************************************************************************/

View File

@@ -0,0 +1,223 @@
import org.gradle.plugins.ide.eclipse.model.SourceFolder
def luceneVersion = '10.5.0'
def luceneRootRelativePath = 'third-party/lucene'
def luceneSourceArtifacts = ['lucene-analysis-common', 'lucene-analyzers-common']
def luceneSourceDirectory = layout.buildDirectory.dir("${luceneRootRelativePath}/source/analyzers-common")
def luceneGeneratedSourceDirectory = layout.buildDirectory.dir('generated/sources/lucene')
def luceneGeneratedPorterFile = luceneGeneratedSourceDirectory.map { it.file('org/egothor/stemmer/benchmark/LucenePorterStemmerCopied.java') }
def luceneSourceDownloadFile = layout.buildDirectory.file("${luceneRootRelativePath}/lucene-${luceneVersion}-sources.jar")
dependencies {
jmhImplementation "org.apache.lucene:lucene-analysis-common:${luceneVersion}"
jmhImplementation "org.apache.lucene:lucene-analysis-stempel:${luceneVersion}"
jmhImplementation "org.apache.lucene:lucene-analysis-morfologik:${luceneVersion}"
}
def buildLuceneSourcesName = { final String artifact ->
"${artifact}-${luceneVersion}-sources.jar"
}
def buildLuceneSourcesUrl = { final String artifact ->
"https://repo1.maven.org/maven2/org/apache/lucene/${artifact}/${luceneVersion}/${buildLuceneSourcesName(artifact)}"
}
def isLuceneSourcesDownloadable = { final String artifact ->
try {
final URL sourceUrl = new URL(buildLuceneSourcesUrl(artifact))
final java.net.HttpURLConnection connection = (java.net.HttpURLConnection) sourceUrl.openConnection()
connection.requestMethod = 'HEAD'
connection.instanceFollowRedirects = true
connection.connectTimeout = 10000
connection.readTimeout = 10000
final int responseCode = connection.responseCode
connection.disconnect()
return responseCode == 200
} catch (Exception ignored) {
return false
}
}
def downloadLuceneSourcesJar = { ->
final File targetFile = luceneSourceDownloadFile.get().asFile
for (String artifact : luceneSourceArtifacts) {
if (!isLuceneSourcesDownloadable(artifact)) {
continue
}
final String sourceUrl = buildLuceneSourcesUrl(artifact)
final File tempFile = new File(targetFile.parentFile, "${artifact}.${luceneVersion}.tmp")
try {
new URL(sourceUrl).withInputStream { inputStream ->
tempFile.parentFile.mkdirs()
tempFile.withOutputStream { outputStream ->
outputStream << inputStream
}
}
if (!tempFile.exists() || tempFile.length() <= 0L) {
throw new GradleException("Downloaded Lucene source artifact for ${artifact} was empty.")
}
targetFile.delete()
if (!tempFile.renameTo(targetFile)) {
throw new GradleException("Failed to persist downloaded Lucene source artifact for ${artifact}.")
}
return
} catch (Exception ignored) {
tempFile.delete()
}
}
throw new GradleException(
"Failed to download Apache Lucene source artifacts ${luceneSourceArtifacts} for version ${luceneVersion}.")
}
def luceneSourceClasspathPath = provider {
project.relativePath(luceneGeneratedSourceDirectory.get().asFile)
}
def luceneEclipseClasspathAttributes = [
gradle_scope : 'jmh',
gradle_used_by_scope: 'jmh',
test : 'true'
]
def isAbsoluteClasspathPath = { String path ->
path.startsWith('/') || path ==~ /^[A-Za-z]:[\\\/].*/
}
def luceneGeneratedPorterNotice = '''
/**
* Generated at benchmark execution time from Apache Lucene source.
*
* This source copy is compiled only for the JMH benchmark source set and is
* not committed as production code.
*/
'''
def transformPorterStemmerSource = { final File sourceFile, final File targetFile ->
if (!sourceFile.exists()) {
throw new GradleException("Apache Lucene PorterStemmer source was not available at ${sourceFile}.")
}
final String sourceText = sourceFile.getText('UTF-8')
String transformedText = sourceText
if (transformedText.contains('package org.apache.lucene.analysis.en;')) {
transformedText = transformedText.replaceFirst(/(?m)^\s*package\s+org\.apache\.lucene\.analysis\.en\s*;/,
'package org.egothor.stemmer.benchmark;')
} else {
throw new GradleException(
'Expected Lucene package-private PorterStemmer in org.apache.lucene.analysis.en package was not found in downloaded source.')
}
transformedText = transformedText.replaceFirst(/(?m)^\s*class\s+PorterStemmer\s*\{/, 'public final class LucenePorterStemmerCopied {')
transformedText = transformedText.replaceFirst(/(?m)^\s*public\s+PorterStemmer\(\)/, 'public LucenePorterStemmerCopied()')
if (!transformedText.contains('class LucenePorterStemmerCopied')) {
throw new GradleException("Failed to rename PorterStemmer class when generating ${targetFile}.")
}
targetFile.parentFile.mkdirs()
targetFile.text = transformedText
}
def resolveLucenePorterStemmerSource = { ->
final File sourceRoot = luceneSourceDirectory.get().asFile
final List<String> candidates = [
'org/apache/lucene/analysis/en/org/apache/lucene/analysis/en/PorterStemmer.java',
'org/apache/lucene/analysis/en/PorterStemmer.java',
'org/apache/lucene/analysis/en/org/tartarus/snowball/ext/PorterStemmer.java'
]
for (String candidate : candidates) {
final File file = new File(sourceRoot, candidate)
if (file.exists()) {
return file
}
}
final FileTree porterCandidates = fileTree(sourceRoot).matching { include '**/PorterStemmer.java' }
for (File file : porterCandidates.files) {
if (file.text.contains('class PorterStemmer') && file.text.contains('package org.apache.lucene.analysis.en;')) {
return file
}
}
throw new GradleException('Unable to resolve Lucene PorterStemmer source file from extracted artifact.')
}
tasks.register('downloadLuceneAnalyzersSources') {
group = 'build setup'
description = 'Downloads Apache Lucene analysis sources for benchmark-only code generation.'
outputs.file(luceneSourceDownloadFile)
doLast {
if (!luceneSourceDownloadFile.get().asFile.exists()) {
downloadLuceneSourcesJar()
}
}
}
tasks.register('extractLuceneAnalyzersSources', Copy) {
group = 'build setup'
description = 'Extracts Apache Lucene analysis source JAR for benchmark-only extraction.'
dependsOn(tasks.named('downloadLuceneAnalyzersSources'))
from(zipTree(luceneSourceDownloadFile))
into(luceneSourceDirectory)
}
tasks.register('generateLucenePorterStemmerCopied') {
group = 'build setup'
description = 'Generates LucenePorterStemmerCopied into the build-only benchmark source directory.'
dependsOn(tasks.named('extractLuceneAnalyzersSources'))
inputs.dir(luceneSourceDirectory)
outputs.file(luceneGeneratedPorterFile)
doLast {
final File sourceFile = resolveLucenePorterStemmerSource()
transformPorterStemmerSource(sourceFile, luceneGeneratedPorterFile.get().asFile)
}
}
sourceSets {
jmh {
java {
srcDir(luceneGeneratedSourceDirectory)
}
}
}
tasks.named('compileJmhJava') {
dependsOn(tasks.named('generateLucenePorterStemmerCopied'))
}
eclipse {
classpath {
file {
whenMerged { classpath ->
String generatedPath = luceneSourceClasspathPath.get()
classpath.entries.removeAll { entry ->
entry.hasProperty('path') && (
entry.path == generatedPath ||
isAbsoluteClasspathPath(entry.path)
)
}
SourceFolder luceneEntry = new SourceFolder(generatedPath, null)
luceneEntry.output = 'bin/jmh'
luceneEclipseClasspathAttributes.each { String name, String value ->
luceneEntry.entryAttributes[name] = value
}
classpath.entries.add(luceneEntry)
}
}
}
}

View File

@@ -51,11 +51,6 @@ publishing {
url = pomLicenseUrl
distribution = pomLicenseDistribution
}
license {
name = pomStemmerDataLicenseName
url = pomStemmerDataLicenseUrl
distribution = pomLicenseDistribution
}
}
developers {
@@ -104,8 +99,6 @@ tasks.register('validateReleaseMetadata') {
if (pomScmDeveloperConnection == null || pomScmDeveloperConnection.isBlank()) missing.add('pomScmDeveloperConnection')
if (pomLicenseName == null || pomLicenseName.isBlank()) missing.add('pomLicenseName')
if (pomLicenseUrl == null || pomLicenseUrl.isBlank()) missing.add('pomLicenseUrl')
if (pomStemmerDataLicenseName == null || pomStemmerDataLicenseName.isBlank()) missing.add('pomStemmerDataLicenseName')
if (pomStemmerDataLicenseUrl == null || pomStemmerDataLicenseUrl.isBlank()) missing.add('pomStemmerDataLicenseUrl')
if (signingKey == null || signingKey.isBlank()) missing.add('pomSigningKey / SIGNING_KEY')
if (signingPassword == null || signingPassword.isBlank()) missing.add('pomSigningPassword / SIGNING_PASSWORD')

View File

@@ -0,0 +1,5 @@
def openNlpVersion = '2.5.4'
dependencies {
jmhImplementation "org.apache.opennlp:opennlp-tools:${openNlpVersion}"
}

View File

@@ -0,0 +1,224 @@
import org.gradle.plugins.ide.eclipse.model.SourceFolder
def paicehuskVersion = 'master'
def paicehuskArchiveName = "paice-husk-stemmer-${paicehuskVersion}.zip"
def paicehuskDownloadUrl = "https://github.com/Hopper262/paice-husk-stemmer/archive/refs/heads/${paicehuskVersion}.zip"
def paicehuskDownloadFile = layout.buildDirectory.file("third-party/paicehusk/${paicehuskArchiveName}")
def paicehuskExtractDirectory = layout.buildDirectory.dir('third-party/paicehusk/source')
def paicehuskArchiveDirectory = paicehuskExtractDirectory.map { it.dir('paice-husk-stemmer-master') }
def paicehuskJavaFile = paicehuskArchiveDirectory.map { it.file('paicehusk_java.java') }
def paicehuskRulesFile = paicehuskArchiveDirectory.map { it.file('paicehusk_rules.txt') }
def paicehuskGeneratedSourceDirectory = layout.buildDirectory.dir('generated/sources/paicehusk')
def paicehuskGeneratedStemmerFile = paicehuskGeneratedSourceDirectory.map { it.file('org/egothor/stemmer/benchmark/PaiceHuskLancasterStemmer.java') }
def paicehuskGeneratedSourcePath = provider {
project.relativePath(paicehuskGeneratedSourceDirectory.get().asFile)
}
def paicehuskSourceEclipseClasspathAttributes = [
gradle_scope : 'jmh',
gradle_used_by_scope: 'jmh',
test : 'true'
]
def paicehuskIsAbsolutePath = { String path ->
path.startsWith('/') || path ==~ /^[A-Za-z]:[\\\/].*/
}
def paicehuskGeneratedNotice = '''
/**
* Generated at benchmark execution time from upstream
* https://github.com/Hopper262/paice-husk-stemmer .
*
* This source copy is compiled only for the JMH benchmark source set and is
* not committed as production code.
*/
'''
def escapeForJava = { final String text ->
return text.replace('\\\\', '\\\\\\\\')
.replace('\"', '\\\"')
}
def toRuleLines = { final File rulesFile ->
final List<String> lines = rulesFile.readLines('UTF-8')
final StringBuilder ruleLines = new StringBuilder()
for (int index = 0; index < lines.size(); index++) {
final String line = lines.get(index)
ruleLines.append(' "')
ruleLines.append(escapeForJava(line))
ruleLines.append('"')
if (index < lines.size() - 1) {
ruleLines.append(',')
}
ruleLines.append('\n')
}
return ruleLines.toString()
}
def paicehuskEngineInsertion = { final String ruleLines ->
return """
public static final String[] RULE_LINES = {
${ruleLines}
};
private static final java.util.HashMap RULES = createRulesFromEmbeddedRules();
/**
* Creates benchmark stemmer instance.
*/
public PaiceHuskLancasterStemmer() {
}
/**
* Applies Paice/Husk stemming to one token.
*
* @param token input token
* @return stemmed token
*/
public String stem(final String token) {
if (token == null) {
return null;
}
return stemWord(token, RULES, null);
}
/**
* Loads bundled rule lines directly from the generated benchmark source.
*
* @return initialized rule map
*/
private static java.util.HashMap createRulesFromEmbeddedRules() {
try {
final java.io.File ruleFile = java.io.File.createTempFile("paicehusk-rules", ".txt");
ruleFile.deleteOnExit();
try (java.io.PrintWriter writer = new java.io.PrintWriter(new java.io.FileWriter(ruleFile))) {
for (String line : RULE_LINES) {
writer.println(line);
}
}
return loadRules(ruleFile.getAbsolutePath());
} catch (Exception exception) {
throw new IllegalStateException("Unable to initialize benchmark Paice/Husk rules.", exception);
}
}
"""
}
def transformPaiceHuskSource = { final File sourceFile, final File rulesFile, final File targetFile ->
if (!sourceFile.exists()) {
throw new GradleException("Paice/Husk Java source was not available at ${sourceFile}.")
}
if (!rulesFile.exists()) {
throw new GradleException("Paice/Husk rule file was not available at ${rulesFile}.")
}
final String sourceText = sourceFile.getText('UTF-8')
String transformedText = sourceText
transformedText = 'package org.egothor.stemmer.benchmark;' + '\n\n' + transformedText
transformedText = transformedText.replaceFirst(/(?m)^\s*class\s+PaiceHusk\s*\{/, 'public final class PaiceHuskLancasterStemmer {')
transformedText = transformedText.replace('new Character(rule.letter)', 'Character.valueOf(rule.letter)')
transformedText = transformedText.replace('new Character(stem.charAt(stem.length() - 1))',
'Character.valueOf(stem.charAt(stem.length() - 1))')
transformedText = transformedText.replaceFirst(/(?m)^(\s*)static HashMap loadRules\(/,
'$1@SuppressWarnings("unchecked")\n$1static HashMap loadRules(')
final int packageEnd = transformedText.indexOf('\n', transformedText.indexOf('package org.egothor.stemmer.benchmark;'))
if (packageEnd >= 0) {
transformedText = transformedText.substring(0, packageEnd + 1) + '\n' + paicehuskGeneratedNotice + transformedText.substring(packageEnd + 1)
}
final String marker = '\n} // end class PaiceHusk'
final int markerIndex = transformedText.lastIndexOf(marker)
if (markerIndex < 0) {
throw new GradleException("Unexpected Paice/Husk source structure at ${sourceFile}.")
}
final String replacement = paicehuskEngineInsertion(toRuleLines(rulesFile))
transformedText = transformedText.substring(0, markerIndex) + '\n' + replacement + '\n}'
targetFile.parentFile.mkdirs()
targetFile.text = transformedText
}
tasks.register('downloadPaiceHuskStemmer') {
group = 'build setup'
description = 'Downloads the upstream Paice/Husk benchmark source for dynamic extraction.'
outputs.file(paicehuskDownloadFile)
doLast {
final File targetFile = paicehuskDownloadFile.get().asFile
targetFile.parentFile.mkdirs()
if (!targetFile.exists()) {
new URL(paicehuskDownloadUrl).withInputStream { inputStream ->
targetFile.withOutputStream { outputStream ->
outputStream << inputStream
}
}
}
}
}
tasks.register('extractPaiceHuskStemmer', Copy) {
group = 'build setup'
description = 'Extracts the upstream Paice/Husk benchmark archive.'
dependsOn(tasks.named('downloadPaiceHuskStemmer'))
from(zipTree(paicehuskDownloadFile))
into(paicehuskExtractDirectory)
}
tasks.register('generatePaiceHuskLancasterStemmer') {
group = 'build setup'
description = 'Generates PaiceHuskLancasterStemmer into a benchmark-only generated source directory.'
dependsOn(tasks.named('extractPaiceHuskStemmer'))
inputs.files(paicehuskJavaFile, paicehuskRulesFile)
outputs.file(paicehuskGeneratedStemmerFile)
doLast {
transformPaiceHuskSource(paicehuskJavaFile.get().asFile, paicehuskRulesFile.get().asFile, paicehuskGeneratedStemmerFile.get().asFile)
}
}
sourceSets {
jmh {
java {
srcDir(paicehuskGeneratedSourceDirectory)
}
}
}
tasks.named('compileJmhJava') {
dependsOn(tasks.named('generatePaiceHuskLancasterStemmer'))
}
eclipse {
classpath {
file {
whenMerged { classpath ->
String generatedPath = paicehuskGeneratedSourcePath.get()
classpath.entries.removeAll { entry ->
entry.hasProperty('path') && (
entry.path == generatedPath ||
paicehuskIsAbsolutePath(entry.path)
)
}
SourceFolder paicehuskEntry = new SourceFolder(generatedPath, null)
paicehuskEntry.output = 'bin/jmh'
paicehuskSourceEclipseClasspathAttributes.each { String name, String value ->
paicehuskEntry.entryAttributes[name] = value
}
classpath.entries.add(paicehuskEntry)
}
}
}
}

View File

@@ -7,13 +7,43 @@ def snowballDistributionDirectoryName = "libstemmer_java-${snowballVersion}"
def snowballRootRelativePath = 'third-party/snowball'
def snowballSourceRelativePath = "${snowballRootRelativePath}/source"
def snowballJavaSourceRelativePath = "${snowballSourceRelativePath}/${snowballDistributionDirectoryName}/java"
def snowballGeneratedSourceRelativePath = 'generated/sources/snowball'
def snowballDownloadUrl = "https://snowballstem.org/dist/${snowballArchiveName}"
def snowballDownloadFile = layout.buildDirectory.file("${snowballRootRelativePath}/${snowballArchiveName}")
def snowballExtractDirectory = layout.buildDirectory.dir(snowballSourceRelativePath)
def snowballJavaSourceDirectory = layout.buildDirectory.dir(snowballJavaSourceRelativePath)
def snowballGeneratedSourceDirectory = layout.buildDirectory.dir(snowballGeneratedSourceRelativePath)
def snowballJavaSourceClasspathPath = provider {
project.relativePath(snowballJavaSourceDirectory.get().asFile)
}
def snowballGeneratedSourceClasspathPath = provider {
project.relativePath(snowballGeneratedSourceDirectory.get().asFile)
}
def transformSnowballSourceText = { final String sourceText ->
String transformedText = sourceText
transformedText = transformedText.replaceAll(/(?m)^\s*package\s+org\.tartarus\.snowball\.ext\s*;/,
'package org.egothor.stemmer.benchmark.snowball.ext;')
transformedText = transformedText.replaceAll(/(?m)^\s*package\s+org\.tartarus\.snowball\s*;/,
'package org.egothor.stemmer.benchmark.snowball;')
transformedText = transformedText.replace('org.tartarus.snowball.', 'org.egothor.stemmer.benchmark.snowball.')
return transformedText
}
def copySnowballSourcesWithPackageIsolation = { final File sourceDirectory, final File targetDirectory ->
final FileTree sourceFiles = fileTree(sourceDirectory).matching { include '**/*.java' }
if (targetDirectory.exists()) {
targetDirectory.deleteDir()
}
for (File sourceFile : sourceFiles.files) {
final String relativePath = sourceDirectory.toPath().relativize(sourceFile.toPath()).toString()
final File outputFile = new File(targetDirectory, relativePath)
outputFile.parentFile.mkdirs()
outputFile.text = transformSnowballSourceText(sourceFile.getText('UTF-8'))
}
}
def snowballEclipseClasspathAttributes = [
gradle_scope : 'jmh',
gradle_used_by_scope: 'jmh',
@@ -53,16 +83,33 @@ tasks.register('extractSnowballJava', Copy) {
into(snowballExtractDirectory)
}
tasks.register('generateIsolatedSnowballSources') {
group = 'build setup'
description = 'Copies Snowball source to benchmark-only package-isolated package paths.'
dependsOn(tasks.named('extractSnowballJava'))
inputs.dir(snowballJavaSourceDirectory)
outputs.dir(snowballGeneratedSourceDirectory)
doLast {
copySnowballSourcesWithPackageIsolation(
snowballJavaSourceDirectory.get().asFile,
snowballGeneratedSourceDirectory.get().asFile
)
}
}
sourceSets {
jmh {
java {
srcDir(snowballJavaSourceDirectory)
srcDir(snowballGeneratedSourceDirectory)
}
}
}
tasks.named('compileJmhJava') {
dependsOn(tasks.named('extractSnowballJava'))
dependsOn(tasks.named('generateIsolatedSnowballSources'))
}
eclipse {
@@ -70,17 +117,19 @@ eclipse {
file {
whenMerged { classpath ->
String generatedSnowballPath = snowballJavaSourceClasspathPath.get()
String generatedIsolatedSnowballPath = snowballGeneratedSourceClasspathPath.get()
String modelSnowballPath = snowballJavaSourceRelativePath
classpath.entries.removeAll { entry ->
entry.hasProperty('path') && (
entry.path == generatedSnowballPath ||
entry.path == generatedIsolatedSnowballPath ||
entry.path == modelSnowballPath ||
isAbsoluteClasspathPath(entry.path)
)
}
SourceFolder snowballEntry = new SourceFolder(generatedSnowballPath, null)
SourceFolder snowballEntry = new SourceFolder(generatedIsolatedSnowballPath, null)
snowballEntry.output = 'bin/jmh'
snowballEclipseClasspathAttributes.each { String name, String value ->
snowballEntry.entryAttributes[name] = value

View File

@@ -248,11 +248,24 @@
<sha256 value="a151df1e2e0b48618d8b06a180748a29b3abb39b1b2396f6a1c879a727488c6e" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.errorprone" name="error_prone_annotations" version="2.47.0">
<artifact name="error_prone_annotations-2.47.0.jar">
<sha256 value="5364bc6f22e72e98195e406a58d3ba1c09ffa11dea0729592cb870dc2de4056d" origin="Generated by Gradle"/>
</artifact>
<artifact name="error_prone_annotations-2.47.0.pom">
<sha256 value="d80c889a4a6f711f6945fbee79e05ec247b178a567e9d5abf58eb26ebf0a0752" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.errorprone" name="error_prone_parent" version="2.41.0">
<artifact name="error_prone_parent-2.41.0.pom">
<sha256 value="c538388d760a5c1c98dcf06f6ed3cfe5f11a651827db5cbd2ed8288c795cad42" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.errorprone" name="error_prone_parent" version="2.47.0">
<artifact name="error_prone_parent-2.47.0.pom">
<sha256 value="2368a990c7a63095e1d0d44459d5a4092f0eb31f8562bd12cdf0e1c877b6a685" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.guava" name="failureaccess" version="1.0.3">
<artifact name="failureaccess-1.0.3.jar">
<sha256 value="cbfc3906b19b8f55dd7cfd6dfe0aa4532e834250d7f080bd8d211a3e246b59cb" origin="Generated by Gradle"/>
@@ -274,6 +287,14 @@
<sha256 value="77ed42c8c8b2cebbb93ac9e07543ff6418aa24bdb8517580cf5324e9a6510956" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.guava" name="guava" version="33.6.0-jre">
<artifact name="guava-33.6.0-jre.jar">
<sha256 value="dc573e1fca4fd5454f4a5fd3d7da2df03002876a4175bafc14a95980dd7713b3" origin="Generated by Gradle"/>
</artifact>
<artifact name="guava-33.6.0-jre.module">
<sha256 value="2baf73ce839ae48e4b9e0083e256b0e58fc3bf8fc78fc3fbe797bbc89011216e" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.guava" name="guava-parent" version="26.0-android">
<artifact name="guava-parent-26.0-android.pom">
<sha256 value="f8698ab46ca996ce889c1afc8ca4f25eb8ac6b034dc898d4583742360016cc04" origin="Generated by Gradle"/>
@@ -294,6 +315,11 @@
<sha256 value="68719e687c6e4c9ff3e0fecbef7bd20896f0f4f7b314743ed33c72f962568215" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.guava" name="guava-parent" version="33.6.0-jre">
<artifact name="guava-parent-33.6.0-jre.pom">
<sha256 value="374bd31f61b1cf612bee9ab2e4d70bbdf77dd85a49b431f809d4fbdc901f2dd4" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="com.google.guava" name="listenablefuture" version="9999.0-empty-to-avoid-conflict-with-guava">
<artifact name="listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar">
<sha256 value="b372a037d4230aa57fbeffdef30fd6123f9c0c2db85d0aced00c91b974f33f99" origin="Generated by Gradle"/>
@@ -524,11 +550,24 @@
<sha256 value="6d849ae7454ab391718e5fc70e2716418ef3ed264472345bd80c6de64e00b6c4" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="me.champeau.jmh" name="jmh-gradle-plugin" version="0.7.3">
<artifact name="jmh-gradle-plugin-0.7.3.jar">
<sha256 value="d7097e619541d90e0a970b2a68573e22ad01d2999ee5365d56d59830765bf98f" origin="Generated by Gradle"/>
</artifact>
<artifact name="jmh-gradle-plugin-0.7.3.module">
<sha256 value="3487d1aba24fe0af527c6d5f78b5f0e8fd64fe9878708b460e6600e39a47bc43" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="me.champeau.jmh" name="me.champeau.jmh.gradle.plugin" version="0.7.2">
<artifact name="me.champeau.jmh.gradle.plugin-0.7.2.pom">
<sha256 value="57e0c23ac60945aefb5a0c4a9339bea68a295364ca47c7a9079a032f79013abb" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="me.champeau.jmh" name="me.champeau.jmh.gradle.plugin" version="0.7.3">
<artifact name="me.champeau.jmh.gradle.plugin-0.7.3.pom">
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<sha256 value="1ad3292cd563381e3fd632f3fded1988f9e9b2be7a9f3db63ff4c4cedba13fa5" origin="Generated by Gradle"/>
@@ -1223,6 +1449,14 @@
<sha256 value="6138300481471c7fe6aeb115f912961f886e1a46ee9c2bd2841b65184824da28" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.codehaus.plexus" name="plexus-utils" version="3.6.1">
<artifact name="plexus-utils-3.6.1.jar">
<sha256 value="05a63effd67e2d6b9d610cc82e2bd7473289d34802e57a529b28110f28af5679" origin="Generated by Gradle"/>
</artifact>
<artifact name="plexus-utils-3.6.1.pom">
<sha256 value="c8397373781af640a76c5da88f1674293b4fc9a2391d0768ee3fc791883b040d" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.codehaus.woodstox" name="stax2-api" version="4.2.2">
<artifact name="stax2-api-4.2.2.jar">
<sha256 value="a61c48d553efad78bc01fffc4ac528bebbae64cbaec170b2a5e39cf61eb51abe" origin="Generated by Gradle"/>
@@ -1247,11 +1481,24 @@
<sha256 value="efe3734bc5b5e390b7ddd5cc7e86a5aca1a0377534e3420962f0931327c88d10" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.cyclonedx" name="cyclonedx-gradle-plugin" version="3.3.0">
<artifact name="cyclonedx-gradle-plugin-3.3.0.jar">
<sha256 value="9bf283e7e451cedf536b263733cf4ddca2329b3cffe19a05ccb8cc1f90a098e8" origin="Generated by Gradle"/>
</artifact>
<artifact name="cyclonedx-gradle-plugin-3.3.0.module">
<sha256 value="92c20482c05782eec05b9c0db1b2ed615160147e52c179c3e080538103e26239" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.cyclonedx.bom" name="org.cyclonedx.bom.gradle.plugin" version="3.2.4">
<artifact name="org.cyclonedx.bom.gradle.plugin-3.2.4.pom">
<sha256 value="9a8e381d2369288b6c3198b3062e8099229abddafd0a49beb631fd999ea07b9a" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.cyclonedx.bom" name="org.cyclonedx.bom.gradle.plugin" version="3.3.0">
<artifact name="org.cyclonedx.bom.gradle.plugin-3.3.0.pom">
<sha256 value="f59df2c670269e7f5e3d9b2b539b9f435db5e88a2d545ab4811f2217d2cc5c68" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.eclipse.ee4j" name="project" version="1.0.5">
<artifact name="project-1.0.5.pom">
<sha256 value="916b4794d8d8220a59a3fdf6a64dbe794aeb23395e888b81ae36a9b5a2c591a6" origin="Generated by Gradle"/>
@@ -1479,6 +1726,14 @@
<sha256 value="08a02856e487c9357f9b29e38745f8ae805848111e72d15aad0352338f1632e1" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.junit" name="junit-bom" version="5.14.4">
<artifact name="junit-bom-5.14.4.module">
<sha256 value="8a5e98d131de7d7aadb1ee88bfd86d66e62a7c2e2a4074a3b2498b03d236eb64" origin="Generated by Gradle"/>
</artifact>
<artifact name="junit-bom-5.14.4.pom">
<sha256 value="5706e8f29a0a07f56efbbea4a0670793414194bb8d24d8143ba1e787a2f32856" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="org.junit" name="junit-bom" version="5.9.3">
<artifact name="junit-bom-5.9.3.module">
<sha256 value="b401fd25901e582a524aa5343c4b39e28bc56e24961c1069bf2b4bbfcee46b93" origin="Generated by Gradle"/>
@@ -1955,6 +2210,14 @@
<sha256 value="ba01ae7a744cb52fe8ecf3b023cbc32e0ccc8c6beef9f26de77a47808239447d" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="ua.net.nlp" name="morfologik-ukrainian-search" version="4.9.1">
<artifact name="morfologik-ukrainian-search-4.9.1.jar">
<sha256 value="463d9054b8d4cfacb9cd69566395826a6fe32fb6e7da91dbf79d37ddf7d56ba0" origin="Generated by Gradle"/>
</artifact>
<artifact name="morfologik-ukrainian-search-4.9.1.pom">
<sha256 value="6a66d8efe6a5c774932401b04411a8feca7d6628631e4ef80819cfafa133dc38" origin="Generated by Gradle"/>
</artifact>
</component>
<component group="us.springett" name="cpe-parser" version="3.0.1">
<artifact name="cpe-parser-3.0.1.jar">
<sha256 value="f98a50dce0a381e08f0f0ac067801c7f4c51dc7f8f1fe7a3c9960c684a809705" origin="Generated by Gradle"/>

View File

@@ -38,17 +38,26 @@ markdown_extensions:
nav:
- Home: index.md
- Getting Started:
- Start:
- Fast Track: fast-track.md
- Quick Start: quick-start.md
- Built-in Languages: built-in-languages.md
- Dictionary Format: dictionary-format.md
- CLI Compilation: cli-compilation.md
- Integration Deep Dive: integration-deep-dive.md
- Programmatic Usage:
- Integration:
- Overview: programmatic-usage.md
- Model Selection and Loading: model-selection-and-loading.md
- Loading and Building Stemmers: programmatic-loading-and-building.md
- Querying and Ambiguity Handling: programmatic-querying-and-ambiguity.md
- Extending and Persisting Compiled Tries: programmatic-extending-and-persistence.md
- Migration and Backward Compatibility: migration-and-backward-compatibility.md
- CLI Compilation: cli-compilation.md
- Dictionaries and Languages:
- Stemmer Models: stemmer-models.md
- Published Model Catalog: stemmer-model-catalog.md
- Built-in Languages: built-in-languages.md
- Dictionary Format: dictionary-format.md
- Contributing Dictionaries: contributing-dictionaries.md
- Architecture and Semantics:
- Overview: architecture-and-reduction.md
@@ -57,11 +66,43 @@ nav:
- Lookup Edge Optimization: lookup-edge-optimization.md
- Compatibility and Guarantees: compatibility-and-guarantees.md
- Dictionaries:
- Contributing Dictionaries: contributing-dictionaries.md
- Benchmarks:
- How to Read Benchmarks: benchmarking.md
- Benchmark Results: benchmarks/index.md
- Reference:
- Methodology: benchmarks/reference/methodology.md
- Linguistic Quality Methodology: benchmarks/reference/linguistic-quality.md
- Tested Stemmers: benchmarks/reference/tested-stemmers.md
- Reproducibility and Raw Data: benchmarks/reference/reproducibility.md
- Corpora: benchmarks/reference/corpora.md
- Environment and Reports: benchmarks/reference/environment.md
- English Dictionary Coverage: benchmarks/reference/english-coverage.md
- Candidate Evaluation: benchmarks/reference/candidates.md
- Language Results:
- Overview: benchmarks/languages/index.md
- Czech: benchmarks/languages/czech.md
- Danish: benchmarks/languages/danish.md
- Dutch: benchmarks/languages/dutch.md
- English: benchmarks/languages/english.md
- Finnish: benchmarks/languages/finnish.md
- French: benchmarks/languages/french.md
- German: benchmarks/languages/german.md
- Hungarian: benchmarks/languages/hungarian.md
- Italian: benchmarks/languages/italian.md
- Norwegian Bokmal: benchmarks/languages/norwegian-bokmal.md
- Norwegian Nynorsk: benchmarks/languages/norwegian-nynorsk.md
- Persian: benchmarks/languages/persian.md
- Polish: benchmarks/languages/polish.md
- Portuguese: benchmarks/languages/portuguese.md
- Russian: benchmarks/languages/russian.md
- Spanish: benchmarks/languages/spanish.md
- Swedish: benchmarks/languages/swedish.md
- Ukrainian: benchmarks/languages/ukrainian.md
- Yiddish: benchmarks/languages/yiddish.md
- Quality and Operations:
- Quality and Operations: quality-and-operations.md
- Benchmarking: benchmarking.md
- Stemming Quality: stemming-quality.md
- Reports: reports.md
- Historical Builds: builds.md
- Test taxonomy and execution filtering: test-taxonomy-and-filtering.md

101
models/bom/build.gradle Normal file
View File

@@ -0,0 +1,101 @@
import groovy.xml.XmlParser
plugins {
id 'java-platform'
id 'maven-publish'
id 'signing'
}
group = 'org.egothor'
version = providers.fileContents(rootProject.layout.projectDirectory.file('models/catalog-version.txt'))
.asText.map(String::trim).get()
Properties modelTopology = new Properties()
rootProject.file('models/model-projects.properties').withInputStream { InputStream input ->
modelTopology.load(input)
}
List<String> modelIds = modelTopology.stringPropertyNames().toList().sort()
dependencies {
constraints {
modelIds.each { String modelId ->
api project(":models:${modelId}")
}
}
}
publishing {
publications {
bom(MavenPublication) {
from components.javaPlatform
artifactId = 'radixor-models-bom'
pom {
name = 'Radixor Stemmer Models BOM'
description = 'Maven dependency-management BOM containing recommended versions for published Radixor models.'
packaging = 'pom'
url = 'https://github.com/leogalambos/Radixor'
licenses {
license {
name = 'BSD-3-Clause'
url = 'https://spdx.org/licenses/BSD-3-Clause.html'
distribution = 'repo'
}
}
developers {
developer {
id = 'egothor'
name = 'Leo Galambos'
email = 'egothor@gmail.com'
}
}
scm {
url = 'https://github.com/leogalambos/Radixor'
connection = 'scm:git:https://github.com/leogalambos/Radixor.git'
developerConnection = 'scm:git:ssh://git@github.com/leogalambos/Radixor.git'
}
}
}
}
repositories {
maven {
name = 'catalogStaging'
url = rootProject.layout.buildDirectory.dir('model-catalog-staging-repository').get().asFile.toURI()
}
}
}
String signingKey = providers.environmentVariable('SIGNING_KEY').orNull
String signingPassword = providers.environmentVariable('SIGNING_PASSWORD').orNull
signing {
required = { providers.environmentVariable('GITHUB_REF_TYPE').orNull == 'tag' }
if (signingKey != null && !signingKey.isBlank()) {
useInMemoryPgpKeys(signingKey, signingPassword)
sign publishing.publications.bom
}
}
tasks.register('verifyPomOnlyPlatform') {
group = 'verification'
description = 'Verifies the POM-only model dependency-management platform.'
dependsOn(tasks.named('generatePomFileForBomPublication'))
doLast {
File pomFile = layout.buildDirectory.file('publications/bom/pom-default.xml').get().asFile
Node pom = new XmlParser().parse(pomFile)
List<Node> constraints = pom.dependencyManagement.dependencies.dependency as List<Node>
List<String> artifactIds = constraints.collect { Node dependency -> dependency.artifactId.text() }
List<String> expected = modelIds.collect { String modelId -> "radixor-model-${modelId}" }
if (pom.packaging.text() != 'pom' || artifactIds != expected) {
throw new GradleException('radixor-models-bom must publish exactly the ordered model constraints as Maven packaging pom.')
}
if (!pom.dependencies.isEmpty()) {
throw new GradleException('radixor-models-bom must not introduce runtime model dependencies.')
}
if (!tasks.withType(Jar).isEmpty()) {
throw new GradleException('radixor-models-bom must not create binary, sources, or Javadoc JARs.')
}
}
}
tasks.named('check') {
dependsOn(tasks.named('verifyPomOnlyPlatform'))
}

View File

@@ -0,0 +1 @@
2026.1

View File

@@ -0,0 +1,23 @@
plugins {
id 'org.egothor.radixor.model'
}
radixorModel {
modelId = 'cs-cz-default'
language = 'CS_CZ'
displayName = 'Czech default model'
defaultModel = true
sourceName = 'UniMorph'
sourceVersion = 'not-recorded-in-legacy-import'
sourceRevision = 'not-recorded-in-legacy-import'
sourceProject = 'UniMorph'
sourceRepository = 'https://github.com/unimorph/ces'
sourceDataset = 'UniMorph Czech morphological dataset (`ces`); repository also documents non-distributed MorfFlex-CZ data'
sourceRevisionStatus = 'not-recorded-in-legacy-import'
sourceLicense = 'CC-BY-SA-3.0'
sourceLicenseUri = 'https://creativecommons.org/licenses/by-sa/3.0/'
sourceAttribution = 'UniMorph; Witold Kieraś is credited for the separate MorfFlex-CZ conversion'
sourceVerificationDate = '2026-07-22'
transformationsSummary = 'Cleaning, normalization, grouping inflected forms by lemma, deduplication, filtering invalid rows, reformatting into Radixor dictionary groups, GZip packaging, and generation of runtime descriptor and checksum metadata'
noticeFileName = 'NOTICE-model-data.txt'
}

View File

@@ -0,0 +1 @@
1.0.0

View File

@@ -0,0 +1,38 @@
Radixor model-data notice
Radixor-derived model data
Copyright (C) 2026, Leo Galambos.
Copyright and, where applicable, database rights are claimed in the
Radixor-specific selection, verification, cleaning, normalization,
grouping, deduplication, filtering, reformatting, metadata preparation,
and packaging of this model, to the extent protected by applicable law.
The underlying morphological data remains attributed to UniMorph and
the upstream contributors identified in this notice.
This derived model data, including Radixor's protectable contributions,
is distributed under Creative Commons Attribution-ShareAlike 3.0
Unported.
Model ID: cs-cz-default
Radixor language: CS_CZ
Source project: UniMorph
Official repository: https://github.com/unimorph/ces
Upstream dataset: UniMorph Czech morphological dataset (`ces`); repository also documents non-distributed MorfFlex-CZ data
Upstream lexical source: Wiktionary; the CC BY-NC-SA MorfFlex-CZ dataset is excluded
Attribution: UniMorph; Witold Kieraś is credited for the separate MorfFlex-CZ conversion
License:
Creative Commons Attribution-ShareAlike 3.0 Unported
Canonical license URI: https://creativecommons.org/licenses/by-sa/3.0/
Source revision: not-recorded-in-legacy-import
Revision status: not-recorded-in-legacy-import
The exact UniMorph commit used for the original Radixor import was not recorded. The model remains attributed to the official UniMorph language repository and is distributed under the repository's stated data license.
Radixor modifications: Cleaning, normalization, grouping inflected forms by lemma, deduplication, filtering invalid rows, reformatting into Radixor dictionary groups, GZip packaging, and generation of runtime descriptor and checksum metadata.
The derived model data is distributed under CC BY-SA 3.0. UniMorph supplies morphological data; Radixor constructs its own patch-command trie at runtime. Neither UniMorph nor any upstream contributor endorses Radixor.
Upstream information verified: 2026-07-22

View File

@@ -0,0 +1,23 @@
plugins {
id 'org.egothor.radixor.model'
}
radixorModel {
modelId = 'da-dk-default'
language = 'DA_DK'
displayName = 'Danish default model'
defaultModel = true
sourceName = 'UniMorph'
sourceVersion = 'not-recorded-in-legacy-import'
sourceRevision = 'not-recorded-in-legacy-import'
sourceProject = 'UniMorph'
sourceRepository = 'https://github.com/unimorph/dan'
sourceDataset = 'UniMorph Danish morphological dataset (`dan`)'
sourceRevisionStatus = 'not-recorded-in-legacy-import'
sourceLicense = 'CC-BY-SA-3.0'
sourceLicenseUri = 'https://creativecommons.org/licenses/by-sa/3.0/'
sourceAttribution = 'UniMorph and Wikipedia contributors'
sourceVerificationDate = '2026-07-22'
transformationsSummary = 'Cleaning, normalization, grouping inflected forms by lemma, deduplication, filtering invalid rows, reformatting into Radixor dictionary groups, GZip packaging, and generation of runtime descriptor and checksum metadata'
noticeFileName = 'NOTICE-model-data.txt'
}

View File

@@ -0,0 +1 @@
1.0.0

View File

@@ -0,0 +1,38 @@
Radixor model-data notice
Radixor-derived model data
Copyright (C) 2026, Leo Galambos.
Copyright and, where applicable, database rights are claimed in the
Radixor-specific selection, verification, cleaning, normalization,
grouping, deduplication, filtering, reformatting, metadata preparation,
and packaging of this model, to the extent protected by applicable law.
The underlying morphological data remains attributed to UniMorph and
the upstream contributors identified in this notice.
This derived model data, including Radixor's protectable contributions,
is distributed under Creative Commons Attribution-ShareAlike 3.0
Unported.
Model ID: da-dk-default
Radixor language: DA_DK
Source project: UniMorph
Official repository: https://github.com/unimorph/dan
Upstream dataset: UniMorph Danish morphological dataset (`dan`)
Upstream lexical source: Wikipedia
Attribution: UniMorph and Wikipedia contributors
License:
Creative Commons Attribution-ShareAlike 3.0 Unported
Canonical license URI: https://creativecommons.org/licenses/by-sa/3.0/
Source revision: not-recorded-in-legacy-import
Revision status: not-recorded-in-legacy-import
The exact UniMorph commit used for the original Radixor import was not recorded. The model remains attributed to the official UniMorph language repository and is distributed under the repository's stated data license.
Radixor modifications: Cleaning, normalization, grouping inflected forms by lemma, deduplication, filtering invalid rows, reformatting into Radixor dictionary groups, GZip packaging, and generation of runtime descriptor and checksum metadata.
The derived model data is distributed under CC BY-SA 3.0. UniMorph supplies morphological data; Radixor constructs its own patch-command trie at runtime. Neither UniMorph nor any upstream contributor endorses Radixor.
Upstream information verified: 2026-07-22

View File

@@ -0,0 +1,23 @@
plugins {
id 'org.egothor.radixor.model'
}
radixorModel {
modelId = 'de-de-default'
language = 'DE_DE'
displayName = 'German default model'
defaultModel = true
sourceName = 'UniMorph'
sourceVersion = 'not-recorded-in-legacy-import'
sourceRevision = 'not-recorded-in-legacy-import'
sourceProject = 'UniMorph'
sourceRepository = 'https://github.com/unimorph/deu'
sourceDataset = 'UniMorph German morphological dataset (`deu`)'
sourceRevisionStatus = 'not-recorded-in-legacy-import'
sourceLicense = 'CC-BY-SA-3.0'
sourceLicenseUri = 'https://creativecommons.org/licenses/by-sa/3.0/'
sourceAttribution = 'UniMorph and English Wiktionary contributors'
sourceVerificationDate = '2026-07-22'
transformationsSummary = 'Cleaning, normalization, grouping inflected forms by lemma, deduplication, filtering invalid rows, reformatting into Radixor dictionary groups, GZip packaging, and generation of runtime descriptor and checksum metadata'
noticeFileName = 'NOTICE-model-data.txt'
}

View File

@@ -0,0 +1 @@
1.0.0

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