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 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.

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

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.

UniDic for morphological analysis is a dictionary for analysis with the morphological analyser MeCab, where the short units exported from the database are used as entries (heading terms).

This module converts English text into numbers. It supports both ordinal and cardinal numbers, negative numbers, and very large numbers.

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

DParser is scannerless GLR parser generator. The form of the text to be parsed can be specified using a combination of regular expressions and grammar productions. Because of the parsing technique, a scannerless GLR parser based on the Tomita algorithm the grammar can be ambiguous, right or left recursive, have any number of null productions, and because there is no separate tokenizer, can include whitespace in terminals and have terminals which are prefixes of other terminals.

This routine applies stemming algorithms to its parameters, returning the stemmed words as appropriate to the selected locale.

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

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

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

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.

Lingua::Stem::Snowball::No is a perl port of the norwegian stemmer at http://snowball.tartarus.org.

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

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

This module attempts to pluralize or singularize short English phrases.

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.

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.

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.

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 a smaller version of the T1 neural network, which is currently one of the best neural networks for Leela Chess Zero.