This module implements a Portuguese stemming algorithm proposed in the paper A Stemming Algorithm for the Portuguese Language by Moreira, V. and Huyck, C.

Hime is an extremely easy-to-use input method framework. It is lightweight, stable, powerful and supports many commonly used input methods, including Cangjie, Zhuyin, Dayi, Ranked, Shrimp, Greek, Anthy, Korean, Latin, Random Cage Fighting Birds, Cool Music etc.

This module provides a regular expression for finding numbers in English text. It also provides functions for extracting and manipulating such numbers.

libskk is a library to deal with Japanese kana-to-kanji conversion method.

This module uses a modified version of the Porter Stemming Algorithm to return a stemmed French word.

This module applies the Porter Stemming Algorithm to its parameters, returning the stemmed Italian word.

Lingua::Stem::Snowball::Da is a perl port of the danish stemmer at http://snowball.sourceforge.net, it was originally altered from the Lingua::Stem::Snowball::Se.

Mecab is a morphological analysis engine developed as a collaboration between the Kyoto university and Nippon Telegraph and Telephone Corporation. The engine is independent of any language, dictionary or corpus.

LexiLang is a natural language detector, designed for handling small text (under 20 characters). It will probably not work reliably for longer text sequences. As it relies on dictionaries, if a word is missing or mispelled, the detection will fail.

SacreMoses is a Python port of Moses' tokenizer, detokenizer, truecaser and punctuation normalizer.

Chewing is an intelligent phonetic (Zhuyin/Bopomofo) input method, one of the most popular choices for Traditional Chinese users.

This module is a rather incomplete implementation of work done by Gudrun Putze-Meier.

Praat is a tool to perform phonetics tasks. It can do speech analysis (pitch, formant, intensity, ...), speech synthesis, labelling, segmenting and manipulation.

This module provides a utility method, "to_identifier" for converting an arbitrary string into a readable representation using the ASCII subset of "\w" for use as an identifier in a computer program. The intent is to make unique identifier names from which the content of the original string can be easily inferred by a human just by reading the identifier.

Lingua::Stem::Snowball::Se is a perl port of the swedish stemmer at http://snowball.sourceforge.net.

Islenska is a Python package that embeds the vocabulary of the Database of Icelandic Morphology and offers various lookups and queries of the data. The database contains over 6.5 million entries, over 3.1 million unique word forms, and about 300,000 distinct lemmas.

This module is a probability based, corpus-trained tagger that assigns part-of-speech tags to English text based on a lookup dictionary and a set of probability values. The tagger assigns appropriate tags based on conditional probabilities - it examines the preceding tag to determine the appropriate tag for the current word. Unknown words are classified according to word morphology or can be set to be treated as nouns or other parts of speech. The tagger also extracts as many nouns and noun phrases as it can, using a set of regular expressions.

Nimf is a lightweight, fast and extensible input method framework. This package provides a fork of the original nimf project, that focuses especially on Korean input (Hangul, Hanja, ...).

The Link Grammar Parser is a syntactic parser of English, Russian, Arabic and Persian (and other languages as well), based on Link Grammar, an original theory of syntax and morphology. Given a sentence, the system assigns to it a syntactic structure, which consists of a set of labelled links connecting pairs of words. The parser also produces a "constituent" (HPSG style phrase tree) representation of a sentence (showing noun phrases, verb phrases, etc.).

This module will tell you if a number, either in words or as digits, is a cardinal or ordinal number.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

This is an official neural network of a ``main run'' of the Leela Chess Zero project. The network was finished being trained in September of 2023.

Maia’s goal is to play the human move, not necessarily the best move. As a result, Maia has a more human-like style than previous engines, matching moves played by human players in online games over 50% of the time.

T1 is currently one of the best neural networks for Leela Chess Zero, however, it was superseded by the neural network T2.