Egothor/Radixor
2
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
main
Radixor/docs/benchmarks/languages/spanish.md
Leo Galambos 5e3d3c7c7d feat(python): add native distribution and release infrastructure 
- add the Rust-backed Python API with PyStemmer compatibility
- distribute standard compiled models as a separate Python package
- generate model artifacts during builds instead of storing them in Git
- add GitHub release and Pages-backed package index workflows
- add Python tests, benchmarks, documentation, and Gradle integration
- refresh the documentation site, branding, and language benchmarks
2026-08-10 22:34:32 +02:00

27 KiB
Raw Permalink Blame History

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. 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 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

Model ID Model version Language Dictionary rows Complete quality tokens Already-root tokens Changed tokens JMH timing tokens
es-es-default 1.0.0 ES_ES 65,059 926,393 120,121 806,272 806,272

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 default-model 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. 8,534 0.921%
BackwardCompoundCommand Applies a multi-step backward patch made from skip, delete, insert, and replace operations. 522,685 56.422%
DeleteSuffixCommand Deletes one or more trailing characters from the word form. 243,410 26.275%
PreserveCommand Returns the word form unchanged because it already matches the preferred root. 124,386 13.427%
ReplaceLastCharacterCommand Replaces the final character of the word form. 27,378 2.955%

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% Radixor dictionary-trained 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 Radixor's dictionary-trained transformation model.
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, 5 warmup iterations, 10 measurement iterations, 3 independent forks, 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 63.164 1.885 78.3 1.000 Radixor dictionary-trained patch-command stemmer.
Lucene HunspellStemFilter luceneHunspellStemFilter 1936.800 18.685 2402.2 30.663 Benchmark-only Spanish Hunspell dictionary compared via Lucene HunspellStemFilter.
Lucene SpanishMinimalStemFilter spanishLuceneSpanishMinimalStemFilter 40.414 1.475 50.1 0.640 Minimal Spanish suffix reducer; narrow baseline.
Lucene SpanishLightStemFilter spanishLuceneSpanishLightStemFilter 43.922 1.497 54.5 0.695 Light Spanish suffix stemmer.
Lucene SpanishPluralStemFilter spanishLuceneSpanishPluralStemFilter 89.799 3.474 111.4 1.422 Plural-oriented Spanish suffix reducer.
Official Snowball direct snowballDirect[SPANISH] 192.868 11.684 239.2 3.053 Official Snowball generated Java stemmer; direct API.
Lucene SnowballFilter luceneSnowballFilter[SPANISH] 182.719 6.957 226.6 2.893 Lucene TokenFilter path around Snowball; includes TokenStream overhead.

Interpretation Notes

  • Radixor is a dictionary-trained patch-command stemmer. Its learned transformations can generalize beyond the word forms listed in the training resource.
  • 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

Runtime performance and linguistic grouping quality are independent dimensions. This section evaluates language ES_ES using the complete validated stemming-quality result matrix. Every distinct surface form is one evaluated item and can belong to several dictionary groups. Two forms are a positive pair when their group-membership sets intersect and a negative pair when those sets are disjoint. A pair shared through several groups is counted once. Exact equality with a predetermined lemma is not required.

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.

Evaluation Scope and Key Findings

The default model is es-es-default, loaded from classpath resource org/egothor/stemmer/models/es-es-default/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 0.989448 among 7 deterministic stemmers. The runner-up is SNOWBALL SPANISH LUCENE FILTER at 0.652438, a difference of 0.337010. This rank does not imply leadership in throughput or every secondary metric.
  • LOWERCASE_GROUPS_ONLY: Radixor ranks first by balanced accuracy at 0.989580 among 7 deterministic stemmers. The runner-up is SNOWBALL SPANISH DIRECT at 0.652542, a difference of 0.337038. 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. PRIMARY_OUTPUT and ALL_CANDIDATES rankings are ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. ANY_CANDIDATE has no single rank metric and is listed alphabetically. Balanced accuracy is a navigation metric, not a universally authoritative quality score.

PRIMARY_OUTPUT ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.989448 0.000000% 2.110334%
2 SNOWBALL SPANISH LUCENE FILTER 0.652438 0.000414% 69.511918%
3 SNOWBALL SPANISH DIRECT 0.652438 0.000413% 69.511932%
4 HUNSPELL SPANISH LUCENE FILTER 0.615028 0.000068% 76.994273%
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER 0.514565 0.000009% 97.087060%
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER 0.503764 0.000002% 99.247265%
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER 0.501678 0.000001% 99.664470%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.978897 1.000000 0.989448 0.999998 0.000002
2 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.895438 0.304881 0.999996 0.652438 0.999915 0.000085
3 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 0.895510 0.304881 0.999996 0.652438 0.999915 0.000085
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.975281 0.230057 0.999999 0.615028 0.999910 0.000090
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 0.974423 0.029129 1.000000 0.514565 0.999887 0.000113
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 0.979154 0.007527 1.000000 0.503764 0.999885 0.000115
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.970596 0.003355 1.000000 0.501678 0.999884 0.000116
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.995707 0.989336 0.983046 0.978897 0.989392 0.989391
2 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.645406 0.454882 0.351206 0.294400 0.522496 0.522469
3 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 0.645436 0.454891 0.351208 0.294407 0.522517 0.522490
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.591847 0.372295 0.271557 0.228724 0.473678 0.473655
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 0.130091 0.056568 0.036142 0.029107 0.168477 0.168467
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 0.036514 0.014940 0.009391 0.007526 0.085851 0.085846
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.016548 0.006687 0.004191 0.003355 0.057067 0.057063
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 41053986 0 885054 360919543590 0 / 360919543590 885054 / 41939040
2 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 12786409 1493087 29152631 360918050503 1493087 / 360919543590 29152631 / 41939040
3 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 12786403 1491944 29152637 360918051646 1491944 / 360919543590 29152637 / 41939040
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 9648381 244539 32290659 360919299051 244539 / 360919543590 32290659 / 41939040
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 1221659 32066 40717381 360919511524 32066 / 360919543590 40717381 / 41939040
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 315690 6721 41623350 360919536869 6721 / 360919543590 41623350 / 41939040
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 140718 4263 41798322 360919539327 4263 / 360919543590 41798322 / 41939040

ANY_CANDIDATE oracle bounds

These results are measured, not missing. ANY_CANDIDATE answers two separate optimistic questions for each pair: a gold-related pair avoids under-stemming when the candidate sets intersect, while a gold-negative pair avoids over-stemming when some non-colliding candidate selection exists. The oracle may choose a different candidate for the same word in different pairs. Consequently, these decisions do not form one globally realizable predicted relation or one TP/FP/FN/TN confusion matrix. Balanced accuracy, F-scores, Jaccard, Fowlkes–Mallows, and MCC are therefore mathematically not applicable, rather than unknown.

Stemmer Optimistic over-stemming (OI) Optimistic under-stemming (UI)
HUNSPELL SPANISH LUCENE FILTER 0.000062% 76.009935%
Radixor 0.000000% 0.001493%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
HUNSPELL SPANISH LUCENE FILTER 223500 / 360919543590 31877837 / 41939040
Radixor 0 / 360919543590 626 / 41939040

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.999993 <0.000001% 0.001493%
2 HUNSPELL SPANISH LUCENE FILTER 0.619950 0.000073% 76.009935%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 0.999959 0.999985 1.000000 0.999993 1.000000 0.000000
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 0.974467 0.239901 0.999999 0.619950 0.999911 0.000089
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 0.999964 0.999972 0.999980 0.999944 0.999972 0.999972
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 0.604361 0.385016 0.282490 0.238402 0.483503 0.483480
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 41938414 1737 626 360919541853 1737 / 360919543590 626 / 41939040
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 10061203 263629 31877837 360919279961 263629 / 360919543590 31877837 / 41939040

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 412822 21039 19090 11309 1.331001% 5 861853
Radixor 884428 0 1737 20967 2.467690% 21 871404

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. PRIMARY_OUTPUT and ALL_CANDIDATES rankings are ordered by unrounded balanced accuracy, followed by MCC, F1, over-stemming rate, over-stemming count, under-stemming rate, and stemmer. ANY_CANDIDATE has no single rank metric and is listed alphabetically. Balanced accuracy is a navigation metric, not a universally authoritative quality score.

PRIMARY_OUTPUT ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 0.989580 <0.000001% 2.084022%
2 SNOWBALL SPANISH DIRECT 0.652542 0.000410% 69.491126%
3 SNOWBALL SPANISH LUCENE FILTER 0.652542 0.000410% 69.491126%
4 HUNSPELL SPANISH LUCENE FILTER 0.614924 0.000068% 77.015229%
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER 0.514575 0.000009% 97.085003%
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER 0.503767 0.000002% 99.246590%
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER 0.501679 0.000001% 99.664108%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 0.999999 0.979160 1.000000 0.989580 0.999998 0.000002
2 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 0.896551 0.305089 0.999996 0.652542 0.999915 0.000085
3 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.896551 0.305089 0.999996 0.652542 0.999915 0.000085
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.975224 0.229848 0.999999 0.614924 0.999910 0.000090
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 0.974539 0.029150 1.000000 0.514575 0.999887 0.000113
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 0.979521 0.007534 1.000000 0.503767 0.999884 0.000116
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.971230 0.003359 1.000000 0.501679 0.999884 0.000116
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.995761 0.989470 0.983258 0.979159 0.989525 0.989523
2 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 0.646055 0.455257 0.351461 0.294714 0.522999 0.522972
3 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.646055 0.455257 0.351461 0.294714 0.522999 0.522972
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 0.591553 0.372016 0.271323 0.228513 0.473448 0.473426
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 0.130175 0.056607 0.036167 0.029128 0.168546 0.168536
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 0.036546 0.014953 0.009400 0.007533 0.085906 0.085901
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 0.016565 0.006695 0.004195 0.003359 0.057116 0.057113
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 40958710 34 871756 359407144881 34 / 359407144915 871756 / 41830466
2 SNOWBALL SPANISH DIRECT PRIMARY_OUTPUT 12762004 1472547 29068462 359405672368 1472547 / 359407144915 29068462 / 41830466
3 SNOWBALL SPANISH LUCENE FILTER PRIMARY_OUTPUT 12762004 1472547 29068462 359405672368 1472547 / 359407144915 29068462 / 41830466
4 HUNSPELL SPANISH LUCENE FILTER PRIMARY_OUTPUT 9614637 244260 32215829 359406900655 244260 / 359407144915 32215829 / 41830466
5 SPANISH LUCENE SPANISH LIGHT STEM FILTER PRIMARY_OUTPUT 1219357 31857 40611109 359407113058 31857 / 359407144915 40611109 / 41830466
6 SPANISH LUCENE SPANISH PLURAL STEM FILTER PRIMARY_OUTPUT 315155 6589 41515311 359407138326 6589 / 359407144915 41515311 / 41830466
7 SPANISH LUCENE SPANISH MINIMAL STEM FILTER PRIMARY_OUTPUT 140505 4162 41689961 359407140753 4162 / 359407144915 41689961 / 41830466

ANY_CANDIDATE oracle bounds

These results are measured, not missing. ANY_CANDIDATE answers two separate optimistic questions for each pair: a gold-related pair avoids under-stemming when the candidate sets intersect, while a gold-negative pair avoids over-stemming when some non-colliding candidate selection exists. The oracle may choose a different candidate for the same word in different pairs. Consequently, these decisions do not form one globally realizable predicted relation or one TP/FP/FN/TN confusion matrix. Balanced accuracy, F-scores, Jaccard, Fowlkes–Mallows, and MCC are therefore mathematically not applicable, rather than unknown.

Stemmer Optimistic over-stemming (OI) Optimistic under-stemming (UI)
HUNSPELL SPANISH LUCENE FILTER 0.000062% 76.037484%
Radixor 0.000000% 0.000000%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
HUNSPELL SPANISH LUCENE FILTER 223274 / 359407144915 31806834 / 41830466
Radixor 0 / 359407144915 0 / 41830466

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 <0.000001% 0.000000%
2 HUNSPELL SPANISH LUCENE FILTER 0.619812 0.000073% 76.037484%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 0.999987 1.000000 1.000000 1.000000 1.000000 0.000000
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 0.974405 0.239625 0.999999 0.619812 0.999911 0.000089
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 0.999989 0.999993 0.999997 0.999987 0.999993 0.999993
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 0.603992 0.384656 0.282183 0.238126 0.483210 0.483187
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 41830466 560 0 359407144355 560 / 359407144915 0 / 41830466
2 HUNSPELL SPANISH LUCENE FILTER ALL_CANDIDATES 10023632 263289 31806834 359406881626 263289 / 359407144915 31806834 / 41830466

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 408995 20986 19029 11287 1.331204% 5 860048
Radixor 871756 34 526 20911 2.466272% 21 869542

Output Policies and Metric Definitions

Each distinct surface form is one item and may belong to several gold groups. Two forms are gold-related when their membership sets intersect; a relation shared by several groups is counted once. PRIMARY_OUTPUT uses one deterministic stem per form. ANY_CANDIDATE is an optimistic oracle-assisted pairwise upper bound: a gold-related pair succeeds when candidates intersect, while a gold-negative pair succeeds when a non-colliding selection exists. Candidate choices may differ between pairs, so this is not deterministic runtime behaviour and does not define one confusion matrix. ALL_CANDIDATES activates every returned candidate; forms are related when candidate sets intersect.

For PRIMARY_OUTPUT and ALL_CANDIDATES, TP = underPossiblePairs - underErrorPairs, FN = underErrorPairs, FP = overErrorPairs, and TN = overPossiblePairs - overErrorPairs. ANY_CANDIDATE publishes only its separate oracle-assisted under/over bounds; confusion-derived metrics are mathematically inapplicable and are not presented in its language-page section. Their machine-readable CSV fields remain empty. Undefined metric denominators in otherwise applicable policies are rendered as n/a.

  • Under-stemming rate (Paice UI): FN / (TP + FN), the false-negative rate over gold-related pairs.
  • Over-stemming rate (Paice OI): FP / (TN + FP), the false-positive rate over gold-negative 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 gold-negative 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).
  • Fowlkes–Mallows 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).

Standard ARI, homogeneity, completeness, V-measure, and NMI are not calculated: their usual contingency-table definitions require an exclusive gold partition, while this gold standard is an overlapping cover.

Provenance

  • Authoritative source: docs/benchmarks/data/stemming-quality.csv
  • Source SHA-256: d34f325da320a2e040b54d8d8b5c216d70448f08cfb8659a423e99882aa1afb5
  • Evaluation command: ./gradlew stemmingQuality --no-daemon
  • 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
  • Model ID, version, and SHA-256: recorded in every CSV row
  • Run date, core source state, JDK, operating system, and hardware: recorded on the benchmark environment page
Reference in New Issue View Git Blame Copy Permalink