matchtext 0.2.3

Fast text/token matching and replacement

Python matchtext

Python 3 package for fast text matching and replacing.

This library implements two fast approaches for matching keywords/gazetteer entries:

• TokenMatcher: keywords/gazetteer entries are sequences of tokens, optionally associated with some data and the matcher tries to match any of those in a given sequence of tokens.
• StringMatcher: keywords/gazetter entries are strings, optionally associated with some data and the matcher tries to match any of those in a given string, optionally only at non-word boundaries.

The matchers are implemented to be fast: TokenMatcher is a hash tree, StringMatcher uses a character trie implementation underneath. Both matchers implement additional features often required in NLP:

• return the offsets in the original iterable where a match occurs
• mapfunc: tokens/characters can be mapped to some canonical form that is used for matching
• ignorefunc: some tokens/characters can be entirely ignored for matching
• match all/longest: only match the longest entry versus all entries
• skip/noskip: if any match is found, continue matching after the longest match versus at the next position