Radixor/docs/benchmarks/languages/czech.md

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

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 one deterministic JMH measurement iteration without warmup. The benchmark may execute the full dictionary pass more than once inside that single timed iteration; percentages divide matching counters by evaluated counters from the same iteration.

Stemmer	All exact	Changed exact	Root preserved	Note
Radixor	99.465%	99.439%	99.582%	Full Radixor dictionary patch-command stemmer.
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	3.117	0.454	66.9	1.000	Full Radixor dictionary patch-command stemmer.
Lucene CzechStemFilter	czechLuceneCzechStemFilter	2.921	0.202	62.7	0.937	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.