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. 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 |
hu-hu-default |
1.0.0 |
HU_HU |
19,406 |
935,713 |
38,775 |
896,938 |
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 default-model 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. |
750,282 |
80.183% |
PreserveCommand |
Returns the word form unchanged because it already matches the preferred root. |
36,139 |
3.862% |
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% |
Radixor dictionary-trained 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 Radixor's dictionary-trained transformation model. |
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 |
hungarianRadixor |
52.020 |
1.337 |
58.0 |
1.000 |
Radixor dictionary-trained patch-command stemmer. |
| Lucene HungarianLightStemFilter |
hungarianLuceneHungarianLightStemFilter |
87.362 |
3.444 |
97.4 |
1.679 |
Light Hungarian suffix stemmer. |
| Official Snowball direct |
snowballDirect[HUNGARIAN] |
158.081 |
9.757 |
176.2 |
3.039 |
Official Snowball generated Java stemmer; direct API. |
| Lucene SnowballFilter |
luceneSnowballFilter[HUNGARIAN] |
179.398 |
8.041 |
200.0 |
3.449 |
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 HU_HU 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 hu-hu-default, loaded from classpath resource org/egothor/stemmer/models/hu-hu-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 0.995555 among 4 deterministic stemmers. The runner-up is SNOWBALL HUNGARIAN LUCENE FILTER at 0.822963, a difference of 0.172592. This rank does not imply leadership in throughput or every secondary metric.
- LOWERCASE_GROUPS_ONLY:
Radixor ranks first by balanced accuracy at 0.996227 among 4 deterministic stemmers. The runner-up is SNOWBALL HUNGARIAN DIRECT at 0.822077, a difference of 0.174151. 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. 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 |
0.995555 |
<0.000001% |
0.889037% |
| 2 |
SNOWBALL HUNGARIAN LUCENE FILTER |
0.822963 |
0.000378% |
35.407050% |
| 3 |
SNOWBALL HUNGARIAN DIRECT |
0.822704 |
0.000309% |
35.458800% |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
0.816967 |
0.000915% |
36.605593% |
Classification metrics
| Rank |
Stemmer |
Output policy |
Precision |
Recall |
Specificity |
Balanced accuracy |
Pairwise accuracy |
Error rate |
| 1 |
Radixor |
PRIMARY_OUTPUT |
0.999998 |
0.991110 |
1.000000 |
0.995555 |
1.000000 |
0.000000 |
| 2 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
0.901236 |
0.645929 |
0.999996 |
0.822963 |
0.999977 |
0.000023 |
| 3 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
0.917622 |
0.645412 |
0.999997 |
0.822704 |
0.999978 |
0.000022 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
0.786953 |
0.633944 |
0.999991 |
0.816967 |
0.999971 |
0.000029 |
Pair-relation metrics
| Rank |
Stemmer |
Output policy |
F0.5 |
F1 |
F2 |
Jaccard |
Fowlkes–Mallows |
MCC |
| 1 |
Radixor |
PRIMARY_OUTPUT |
0.998208 |
0.995534 |
0.992875 |
0.991108 |
0.995544 |
0.995544 |
| 2 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
0.835212 |
0.752518 |
0.684724 |
0.603229 |
0.762978 |
0.762967 |
| 3 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
0.846240 |
0.757814 |
0.686119 |
0.610064 |
0.769574 |
0.769564 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
0.750715 |
0.702210 |
0.659593 |
0.541082 |
0.706318 |
0.706304 |
Raw pair counts
| Rank |
Stemmer |
Output policy |
TP |
FP |
FN |
TN |
Over error / possible |
Under error / possible |
| 1 |
Radixor |
PRIMARY_OUTPUT |
21921219 |
39 |
196636 |
414653743434 |
39 / 414653743473 |
196636 / 22117855 |
| 2 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
14286575 |
1565633 |
7831280 |
414652177840 |
1565633 / 414653743473 |
7831280 / 22117855 |
| 3 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
14275129 |
1281527 |
7842726 |
414652461946 |
1281527 / 414653743473 |
7842726 / 22117855 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
14021483 |
3795942 |
8096372 |
414649947531 |
3795942 / 414653743473 |
8096372 / 22117855 |
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% |
Oracle-bound pair counts
| Stemmer |
Unavoidable over errors / gold-negative pairs |
Unrepairable under errors / gold-related pairs |
| Radixor |
0 / 414653743473 |
0 / 22117855 |
ALL_CANDIDATES ranking
| Rank |
Stemmer |
Balanced accuracy |
Over-stemming (OI) |
Under-stemming (UI) |
| 1 |
Radixor |
1.000000 |
<0.000001% |
0.000000% |
Classification metrics
| Rank |
Stemmer |
Output policy |
Precision |
Recall |
Specificity |
Balanced accuracy |
Pairwise accuracy |
Error rate |
| 1 |
Radixor |
ALL_CANDIDATES |
0.999991 |
1.000000 |
1.000000 |
1.000000 |
1.000000 |
0.000000 |
Pair-relation metrics
| Rank |
Stemmer |
Output policy |
F0.5 |
F1 |
F2 |
Jaccard |
Fowlkes–Mallows |
MCC |
| 1 |
Radixor |
ALL_CANDIDATES |
0.999993 |
0.999996 |
0.999998 |
0.999991 |
0.999996 |
0.999996 |
Raw pair counts
| Rank |
Stemmer |
Output policy |
TP |
FP |
FN |
TN |
Over error / possible |
Under error / possible |
| 1 |
Radixor |
ALL_CANDIDATES |
22117855 |
192 |
0 |
414653743281 |
192 / 414653743473 |
0 / 22117855 |
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 |
196636 |
39 |
153 |
6664 |
0.731754% |
5 |
917595 |
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. 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 |
0.996227 |
<0.000001% |
0.754564% |
| 2 |
SNOWBALL HUNGARIAN DIRECT |
0.822077 |
0.000334% |
35.584346% |
| 3 |
SNOWBALL HUNGARIAN LUCENE FILTER |
0.822077 |
0.000334% |
35.584346% |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
0.815385 |
0.000869% |
36.922109% |
Classification metrics
| Rank |
Stemmer |
Output policy |
Precision |
Recall |
Specificity |
Balanced accuracy |
Pairwise accuracy |
Error rate |
| 1 |
Radixor |
PRIMARY_OUTPUT |
0.999998 |
0.992454 |
1.000000 |
0.996227 |
1.000000 |
0.000000 |
| 2 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
0.915319 |
0.644157 |
0.999997 |
0.822077 |
0.999977 |
0.000023 |
| 3 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
0.915319 |
0.644157 |
0.999997 |
0.822077 |
0.999977 |
0.000023 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
0.802756 |
0.630779 |
0.999991 |
0.815385 |
0.999971 |
0.000029 |
Pair-relation metrics
| Rank |
Stemmer |
Output policy |
F0.5 |
F1 |
F2 |
Jaccard |
Fowlkes–Mallows |
MCC |
| 1 |
Radixor |
PRIMARY_OUTPUT |
0.998480 |
0.996212 |
0.993954 |
0.992453 |
0.996219 |
0.996219 |
| 2 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
0.844241 |
0.756163 |
0.684726 |
0.607928 |
0.767860 |
0.767849 |
| 3 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
0.844241 |
0.756163 |
0.684726 |
0.607928 |
0.767860 |
0.767849 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
0.761246 |
0.706452 |
0.659016 |
0.546135 |
0.711591 |
0.711577 |
Raw pair counts
| Rank |
Stemmer |
Output policy |
TP |
FP |
FN |
TN |
Over error / possible |
Under error / possible |
| 1 |
Radixor |
PRIMARY_OUTPUT |
21206087 |
39 |
161230 |
380936197647 |
39 / 380936197686 |
161230 / 21367317 |
| 2 |
SNOWBALL HUNGARIAN DIRECT |
PRIMARY_OUTPUT |
13763897 |
1273370 |
7603420 |
380934924316 |
1273370 / 380936197686 |
7603420 / 21367317 |
| 3 |
SNOWBALL HUNGARIAN LUCENE FILTER |
PRIMARY_OUTPUT |
13763897 |
1273370 |
7603420 |
380934924316 |
1273370 / 380936197686 |
7603420 / 21367317 |
| 4 |
HUNGARIAN LUCENE HUNGARIAN LIGHT STEM FILTER |
PRIMARY_OUTPUT |
13478053 |
3311675 |
7889264 |
380932886011 |
3311675 / 380936197686 |
7889264 / 21367317 |
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% |
Oracle-bound pair counts
| Stemmer |
Unavoidable over errors / gold-negative pairs |
Unrepairable under errors / gold-related pairs |
| Radixor |
0 / 380936197686 |
0 / 21367317 |
ALL_CANDIDATES ranking
| Rank |
Stemmer |
Balanced accuracy |
Over-stemming (OI) |
Under-stemming (UI) |
| 1 |
Radixor |
1.000000 |
<0.000001% |
0.000000% |
Classification metrics
| Rank |
Stemmer |
Output policy |
Precision |
Recall |
Specificity |
Balanced accuracy |
Pairwise accuracy |
Error rate |
| 1 |
Radixor |
ALL_CANDIDATES |
0.999991 |
1.000000 |
1.000000 |
1.000000 |
1.000000 |
0.000000 |
Pair-relation metrics
| Rank |
Stemmer |
Output policy |
F0.5 |
F1 |
F2 |
Jaccard |
Fowlkes–Mallows |
MCC |
| 1 |
Radixor |
ALL_CANDIDATES |
0.999993 |
0.999996 |
0.999998 |
0.999991 |
0.999996 |
0.999996 |
Raw pair counts
| Rank |
Stemmer |
Output policy |
TP |
FP |
FN |
TN |
Over error / possible |
Under error / possible |
| 1 |
Radixor |
ALL_CANDIDATES |
21367317 |
192 |
0 |
380936197494 |
192 / 380936197686 |
0 / 21367317 |
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 |
161230 |
39 |
153 |
5518 |
0.632162% |
5 |
878574 |
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:
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
- 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
Leo Galambos
5e3d3c7c7d
