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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. 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
nl-nl-default 1.0.0 NL_NL 4,992 31,466 9,981 21,485 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 default-model 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% Radixor dictionary-trained 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, 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 radixor[DUTCH] 1.340 0.127 62.4 1.000 Radixor dictionary-trained patch-command stemmer.
Lucene HunspellStemFilter luceneHunspellStemFilter 22.275 2.325 1036.8 16.621 Benchmark-only Dutch Hunspell dictionary compared via Lucene HunspellStemFilter.
Official Snowball direct snowballDirect[DUTCH] 4.298 0.185 200.0 3.207 Official Snowball generated Java stemmer; direct API.
Lucene SnowballFilter luceneSnowballFilter[DUTCH] 7.317 0.255 340.6 5.460 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 NL_NL 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 nl-nl-default, loaded from classpath resource org/egothor/stemmer/models/nl-nl-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 0.988733 among 4 deterministic stemmers. The runner-up is SNOWBALL DUTCH DIRECT at 0.727093, a difference of 0.261640. This rank does not imply leadership in throughput or every secondary metric.
  • LOWERCASE_GROUPS_ONLY: Radixor ranks first by balanced accuracy at 0.989114 among 4 deterministic stemmers. The runner-up is SNOWBALL DUTCH DIRECT at 0.730509, a difference of 0.258605. 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 0.988733 0.000000% 2.253364%
2 SNOWBALL DUTCH DIRECT 0.727093 0.000870% 54.580443%
3 HUNSPELL DUTCH LUCENE FILTER 0.642844 0.000104% 71.431010%
4 SNOWBALL DUTCH LUCENE FILTER 0.617975 0.000221% 76.404861%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.977466 1.000000 0.988733 0.999996 0.000004
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 0.907391 0.454196 0.999991 0.727093 0.999889 0.000111
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.981029 0.285690 0.999999 0.642844 0.999865 0.000135
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.952453 0.235951 0.999998 0.617975 0.999854 0.000146
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.995411 0.988605 0.981891 0.977466 0.988669 0.988667
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 0.756437 0.605372 0.504600 0.434074 0.641976 0.641934
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.659835 0.442513 0.332878 0.284120 0.529405 0.529368
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.592568 0.378209 0.277738 0.233204 0.474060 0.474022
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 62985 0 1452 343168663 0 / 343168663 1452 / 64437
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 29267 2987 35170 343165676 2987 / 343168663 35170 / 64437
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 18409 356 46028 343168307 356 / 343168663 46028 / 64437
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 15204 759 49233 343167904 759 / 343168663 49233 / 64437

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 DUTCH LUCENE FILTER 0.000096% 66.975495%
Radixor 0.000000% 0.000000%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
HUNSPELL DUTCH LUCENE FILTER 330 / 343168663 43157 / 64437
Radixor 0 / 343168663 0 / 64437

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 0.000000% 0.000000%
2 HUNSPELL DUTCH LUCENE FILTER 0.665122 0.000147% 66.975495%
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 DUTCH LUCENE FILTER ALL_CANDIDATES 0.976909 0.330245 0.999999 0.665122 0.999873 0.000127
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 DUTCH LUCENE FILTER ALL_CANDIDATES 0.701991 0.493621 0.380638 0.327687 0.567996 0.567958
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 64437 0 0 343168663 0 / 343168663 0 / 64437
2 HUNSPELL DUTCH LUCENE FILTER ALL_CANDIDATES 21280 503 43157 343168160 503 / 343168663 43157 / 64437

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 2871 26 147 1199 4.576161% 3 27429
Radixor 1452 0 0 296 1.129728% 3 26501

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 0.989114 0.000000% 2.177156%
2 SNOWBALL DUTCH DIRECT 0.730509 0.000926% 53.897299%
3 HUNSPELL DUTCH LUCENE FILTER 0.644879 0.000103% 71.024152%
4 SNOWBALL DUTCH LUCENE FILTER 0.618013 0.000222% 76.397220%
Classification metrics
Rank Stemmer Output policy Precision Recall Specificity Balanced accuracy Pairwise accuracy Error rate
1 Radixor PRIMARY_OUTPUT 1.000000 0.978228 1.000000 0.989114 0.999996 0.000004
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 0.906773 0.461027 0.999991 0.730509 0.999885 0.000115
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.982090 0.289758 0.999999 0.644879 0.999860 0.000140
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.954134 0.236028 0.999998 0.618013 0.999849 0.000151
Pair-relation metrics
Rank Stemmer Output policy F0.5 F1 F2 Jaccard Fowlkes–Mallows MCC
1 Radixor PRIMARY_OUTPUT 0.995569 0.988994 0.982507 0.978228 0.989054 0.989052
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 0.759842 0.611269 0.511295 0.440164 0.646565 0.646521
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.664532 0.447489 0.337317 0.288235 0.533450 0.533411
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 0.593185 0.378440 0.277851 0.233380 0.474555 0.474515
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor PRIMARY_OUTPUT 61646 0 1372 322555083 0 / 322555083 1372 / 63018
2 SNOWBALL DUTCH DIRECT PRIMARY_OUTPUT 29053 2987 33965 322552096 2987 / 322555083 33965 / 63018
3 HUNSPELL DUTCH LUCENE FILTER PRIMARY_OUTPUT 18260 333 44758 322554750 333 / 322555083 44758 / 63018
4 SNOWBALL DUTCH LUCENE FILTER PRIMARY_OUTPUT 14874 715 48144 322554368 715 / 322555083 48144 / 63018

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 DUTCH LUCENE FILTER 0.000095% 66.488940%
Radixor 0.000000% 0.000000%
Oracle-bound pair counts
Stemmer Unavoidable over errors / gold-negative pairs Unrepairable under errors / gold-related pairs
HUNSPELL DUTCH LUCENE FILTER 307 / 322555083 41900 / 63018
Radixor 0 / 322555083 0 / 63018

ALL_CANDIDATES ranking

Rank Stemmer Balanced accuracy Over-stemming (OI) Under-stemming (UI)
1 Radixor 1.000000 0.000000% 0.000000%
2 HUNSPELL DUTCH LUCENE FILTER 0.667555 0.000148% 66.488940%
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 DUTCH LUCENE FILTER ALL_CANDIDATES 0.977912 0.335111 0.999999 0.667555 0.999869 0.000131
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 DUTCH LUCENE FILTER ALL_CANDIDATES 0.706770 0.499167 0.385834 0.332593 0.572458 0.572419
Raw pair counts
Rank Stemmer Output policy TP FP FN TN Over error / possible Under error / possible
1 Radixor ALL_CANDIDATES 63018 0 0 322555083 0 / 322555083 0 / 63018
2 HUNSPELL DUTCH LUCENE FILTER ALL_CANDIDATES 21118 477 41900 322554606 477 / 322555083 41900 / 63018

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 2858 26 144 1131 4.452405% 3 26562
Radixor 1372 0 0 273 1.074719% 3 25679

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: 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
  • 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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