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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. 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
cs-cz-default 1.0.0 CS_CZ 5,113 56,612 10,049 46,563 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 default-model 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. 711 1.256%
BackwardCompoundCommand Applies a multi-step backward patch made from skip, delete, insert, and replace operations. 22,643 39.997%
DeleteSuffixCommand Deletes one or more trailing characters from the word form. 15,007 26.509%
PreserveCommand Returns the word form unchanged because it already matches the preferred root. 10,046 17.745%
ReplaceLastCharacterCommand Replaces the final character of the word form. 8,205 14.493%

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% Radixor dictionary-trained 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.
Official Snowball direct 19.865% 18.186% 27.645% Official Snowball 3.1.0 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 czechRadixor 3.230 0.050 69.4 1.000 Radixor dictionary-trained patch-command stemmer.
Lucene HunspellStemFilter luceneHunspellStemFilter 349.111 24.459 7497.6 108.091 Benchmark-only Czech Hunspell dictionary compared via Lucene HunspellStemFilter.
Lucene CzechStemFilter czechLuceneCzechStemFilter 2.927 0.032 62.9 0.906 Czech suffix stemmer implemented as a Lucene TokenFilter.
Official Snowball direct snowballDirect[CZECH] 3.835 0.320 82.4 1.187 Official Snowball 3.1.0 generated Java stemmer; direct API.

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 CS_CZ 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 cs-cz-default, loaded from classpath resource org/egothor/stemmer/models/cs-cz-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 0.996617 among 4 deterministic stemmers. The runner-up is HUNSPELL CZECH LUCENE FILTER at 0.854132, a difference of 0.142485. This rank does not imply leadership in throughput or every secondary metric.
  • LOWERCASE_GROUPS_ONLY: Radixor ranks first by balanced accuracy at 0.997195 among 4 deterministic stemmers. The runner-up is HUNSPELL CZECH LUCENE FILTER at 0.853150, a difference of 0.144045. 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. 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 0.996617 0.000000% 0.676519%
2 HUNSPELL CZECH LUCENE FILTER 0.854132 0.000691% 29.172837%
3 CZECH LUCENE CZECH STEM FILTER 0.794343 0.000928% 41.130549%
4 SNOWBALL CZECH DIRECT 0.786366 0.000904% 42.725842%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.993235 1.000000 0.996617 0.999998 0.000002
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 0.958877 0.708272 0.999993 0.854132 0.999927 0.000073
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 0.935210 0.588695 0.999991 0.794343 0.999897 0.000103
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 0.935153 0.572742 0.999991 0.786366 0.999894 0.000106
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.998640 0.996606 0.994581 0.993235 0.996612 0.996611
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 0.895506 0.814739 0.747335 0.687392 0.824103 0.824070
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 0.836710 0.722556 0.635811 0.565626 0.741992 0.741949
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 0.830101 0.710396 0.620864 0.550864 0.731848 0.731804
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 298476 0 2033 1320705191 0 / 1320705191 2033 / 300509
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 212842 9128 87667 1320696063 9128 / 1320705191 87667 / 300509
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 176908 12256 123601 1320692935 12256 / 1320705191 123601 / 300509
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 172114 11935 128395 1320693256 11935 / 1320705191 128395 / 300509

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)
Radixor 0.000000% 0.000000%
HUNSPELL CZECH LUCENE FILTER 0.000650% 25.611213%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
Radixor 0 / 1320705191 0 / 300509
HUNSPELL CZECH LUCENE FILTER 8582 / 1320705191 76964 / 300509

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 0.000000% 0.000000%
2 HUNSPELL CZECH LUCENE FILTER 0.871940 0.000816% 25.611213%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 0.954016 0.743888 0.999992 0.871940 0.999934 0.000066
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 0.903001 0.835949 0.778167 0.718138 0.842426 0.842395
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 300509 0 0 1320705191 0 / 1320705191 0 / 300509
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 223545 10775 76964 1320694416 10775 / 1320705191 76964 / 300509

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 2033 0 0 321 0.624501% 4 51739
HUNSPELL CZECH LUCENE FILTER 10703 546 1647 3194 6.213887% 5 55179

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. 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 0.997195 0.000000% 0.561033%
2 HUNSPELL CZECH LUCENE FILTER 0.853150 0.000700% 29.369351%
3 CZECH LUCENE CZECH STEM FILTER 0.792522 0.000918% 41.494586%
4 SNOWBALL CZECH DIRECT 0.784821 0.000923% 43.034822%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.994390 1.000000 0.997195 0.999999 0.000001
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 0.958957 0.706306 0.999993 0.853150 0.999925 0.000075
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 0.936557 0.585054 0.999991 0.792522 0.999895 0.000105
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 0.934577 0.569652 0.999991 0.784821 0.999891 0.000109
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.998873 0.997187 0.995507 0.994390 0.997191 0.997190
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 0.894932 0.813466 0.745594 0.685581 0.822993 0.822960
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 0.836092 0.720206 0.632534 0.562751 0.740227 0.740184
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 0.828436 0.707849 0.617907 0.547807 0.729646 0.729601
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 295818 0 1669 1284770069 0 / 1284770069 1669 / 297487
2 HUNSPELL CZECH LUCENE FILTER PRIMARY_OUTPUT 210117 8993 87370 1284761076 8993 / 1284770069 87370 / 297487
3 CZECH LUCENE CZECH STEM FILTER PRIMARY_OUTPUT 174046 11790 123441 1284758279 11790 / 1284770069 123441 / 297487
4 SNOWBALL CZECH DIRECT PRIMARY_OUTPUT 169464 11863 128023 1284758206 11863 / 1284770069 128023 / 297487

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)
Radixor 0.000000% 0.000000%
HUNSPELL CZECH LUCENE FILTER 0.000663% 25.840457%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
Radixor 0 / 1284770069 0 / 297487
HUNSPELL CZECH LUCENE FILTER 8518 / 1284770069 76872 / 297487

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 0.000000% 0.000000%
2 HUNSPELL CZECH LUCENE FILTER 0.870794 0.000819% 25.840457%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor ALL_CANDIDATES 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 0.954473 0.741595 0.999992 0.870794 0.999932 0.000068
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor ALL_CANDIDATES 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 0.902651 0.834675 0.776220 0.716259 0.841328 0.841297
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 297487 0 0 1284770069 0 / 1284770069 0 / 297487
2 HUNSPELL CZECH LUCENE FILTER ALL_CANDIDATES 220615 10523 76872 1284759546 10523 / 1284770069 76872 / 297487

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 1669 0 0 269 0.530603% 4 50975
HUNSPELL CZECH LUCENE FILTER 10498 475 1530 3117 6.148293% 5 54394

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: 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
  • 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
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