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RADIXOR
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Dictionary-trained. Not dictionary-bound.

High-quality multilingual stemming.
Built for Java and Python.

Radixor learns word-to-stem transformations from lexical evidence, compiles them into a reduced trie of patch commands, and applies those commands algorithmically at runtime. The training dictionary supplies evidence—it does not define a closed runtime vocabulary.

Choose your runtime
♨Get Radixor for Java → PyGet Radixor for Python →
Learned transformationsTrie + compact patch commands
GeneralizesNot restricted to listed word forms
ReproduciblePublic quality and speed evidence
Why Radixor is different → See the benchmarks → Choose a documentation path →
Java benchmark award

Java: wins in all 20
benchmarked languages

In the current same-language Java matrix, Radixor records the lowest measured runtime against every configured comparator on all 20 language pages.

20 / 20Benchmark winsAcross all 20 languages
Reproducible · Transparent · Verifiable

Every result is public, verifiable, and reproducible.

View all benchmarks →

Quality and speed you can trust — Finnish case study

Radixor❄ Snowball Finnish Radixor advantage
Quality score(higher is better)0.9848~0.740+33% higher quality
◌Overstemming(lower is better)3.03%~52%94.2% lower
Speed(vs. PyStemmer)1.14× faster1.00× (baseline)1.14× faster

Quality comparator: published Snowball Finnish Lucene/Snowball results. Speed comparator: PyStemmer 3.1.0 at batch size N=100. The point is the combined quality/performance envelope, not wrapper identity.

20 languagesFull Unicode support
Czech flagCzechDanish flagDanishDutch flagDutchEnglish flagEnglishFinnish flagFinnishFrench flagFrenchGerman flagGermanHebrew flagHebrewHungarian flagHungarianItalian flagItalianNorwegian Bokmål flagNorwegian BokmålNorwegian Nynorsk flagNorwegian NynorskPersian flagPersianPolish flagPolishPortuguese flagPortugueseRussian flagRussianSpanish flagSpanishSwedish flagSwedishUkrainian flagUkrainianYiddish flagYiddish
♨Java
import org.egothor.stemmer.StemmerPatchTrieLoader;
import org.egothor.stemmer.FrequencyTrie;

FrequencyTrie<CompiledPatchCommand> trie =
    StemmerPatchTrieLoader.loadCompiled(
        StemmerPatchTrieLoader.Language.US_UK, ...);

String word = "running";
CompiledPatchCommand patch = trie.get(word);
String stem = patch == null ? word : patch.apply(word);
View Java docs →
PyPython
from radixor import Stemmer

stemmer = Stemmer("en")
stems = stemmer.stem_batch([
    "running", "studies", "better", "cars"
    ])
View Python docs →

Learned transformationsLexical evidence becomes
compact patch commands

Beyond the dictionaryRuntime is not a closed
word-to-lemma lookup

Native speedMulti-million words per
second in Python and Java

20 language modelsOne architecture, trained
from language-specific data

Production readyDeterministic behavior
and reproducible evidence

A different stemming architecture.

Radixor combines learned patch commands, trie-based structural sharing, subtree reduction, and deterministic runtime application. It is neither a flat dictionary lookup nor another fixed suffix-rule table.

Technology & lineage ▣  Read the Docs
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