Features overview

Every subsystem below is implemented, tested, and documented. Start from what you want to do, or from the crate map further down.

Start from the problem

I want to…	Use
Split text into words	Tokenizers — start with AggressiveTokenizer
Split text into sentences	SentenceTokenizer
Tokenize a language other than English	Tokenizers — 16 language variants
Measure how similar two strings are	String distance
Correct a typo	levenshtein, usually after narrowing candidates
Correct spelling against a corpus	Spellcheck
Match names that sound alike	Phonetics
Search a whole dictionary for sound-alikes	Phonetic neighbors
Match names across language boundaries (genealogy)	Beider-Morse
Match names with a shared prefix	jaro_winkler
Work out which encoder or stemmer a text even needs	Language
Build an autocomplete index	Trie
Find repeated phrases	N-grams
Strip accents before comparing	remove_diacritics
Expand English contractions	normalize
Normalise Japanese width and kana	normalize_ja
Romanise Japanese kana	Transliterators
Pluralise a noun, or write "23rd"	Inflectors
Reduce words to a stem	Stemmers
Rank documents by relevance	TF-IDF
Classify documents into categories	Classifiers
Score sentiment over a token list	Sentiment
Look up synonyms and word relations	WordNet
Tag parts of speech	POS tagger
Split a tagged sentence into subject and predicate	Sentence analyzers
Write generic code over any tokenizer	Core vocabulary

The crates

Subsystem	Crate	Public surface
Tokenizers	verbora-tokenizers	25 tokenizers, Tokenize, Utf16Token
String distance	verbora-distance	8 metrics, 5 StringMetric impls
Phonetics	verbora-phonetics	11 encoders: SoundEx, Metaphone, Double Metaphone, Daitch–Mokotoff ×2, Cologne, Nysiis, Caverphone 1/2, Phonex, Refined Soundex, Match Rating
Phonetic neighbors	verbora-phonetics	PhoneticIndex — dictionary-wide candidate generation over any of the four core encoders
Beider-Morse	verbora-phonetics	Cross-language surname matching over up to 18 languages at once, with auto-detection
Language	verbora-language	Script detection, optional statistical language detection, and recommend() — language → phonetic encoder
N-grams	verbora-ngrams	window engine, stats, Chinese n-grams
Normalizers	verbora-normalizers	6 normalizers, 17 Japanese converters
Inflectors	verbora-inflectors	6 inflectors, runtime rules
Trie	verbora-trie	prefix tree, prefix and path queries
Transliterators	verbora-transliterators	Japanese kana → romaji, five-phase pipeline
WordNet	verbora-wordnet	lexical database, synsets, relation traversal, 4 storage strategies
TF-IDF	verbora-tfidf	term interning, incremental idf cache, listTerms/tfidf/tfidfs
Sentiment	verbora-sentiment	14 lexicons across 10 languages, sticky negation
Classifiers	verbora-classifiers	Bayes, logistic regression, MaxEnt + GIS
Stemmers	verbora-stemmers	16 stemmers: Porter/Snowball across 12 languages, Lancaster, Japanese, Indonesian
Spellcheck	verbora-spellcheck	frequency-ranked correction, BK-tree and deletion indexes
POS tagger	verbora-tagger	Brill tagging, training and evaluation; English and Dutch data
Sentence analyzers	verbora-analyzers	phrase annotation, subject/predicate splitting, sentence type
Utilities	verbora-util	stop words, abbreviations, weighted graphs and storage backends
Core vocabulary	verbora-core	6 traits, Token, StopWords, whitespace helpers

Language support

Language	Tokenizer	Normalizer	Inflector	Phonetics
English	✅	✅	✅ nouns, verbs, ordinals	✅ all four core encoders
French	✅		✅ nouns, ordinals
German	✅
Spanish	✅
Italian	✅
Portuguese	✅
Dutch	✅
Norwegian	✅	✅
Swedish	✅	✅
Danish/Nordic		✅
Russian	✅
Ukrainian	✅
Polish	✅
Persian	✅
Hindi	✅
Indonesian	✅
Vietnamese	✅
Finnish	✅ OrthographyTokenizer
Japanese	✅	✅ + 17 converters	✅ nouns
Chinese	✅ n-grams

Latin-script diacritic folding via remove_diacritics applies far more broadly than the Normalizer column suggests — it is a table over 820 non-ASCII characters, not a per-language rule set. Stemmers cover 12 languages on their own axis; see Stemmers.

Where else to look

• Choosing the right API — cross-subsystem decisions.
• Performance — allocation, zero-copy, parallelism.
• Benchmarks — measured numbers and how to reproduce.
• Recipes — end-to-end pipelines.
• Status and scope — what "available" means, and the boundaries.
• Exact signatures live in rustdoc; these pages cover selection, composition, costs and common mistakes.