# Create a parser object
my $p = new Lingua::EN::Tagger;
# Add part of speech tags to a text
my $tagged_text = $p->add_tags($text);
# Get a list of all nouns and noun phrases with occurrence counts
my %word_list = $p->get_words($text);
# Get a readable version of the tagged text
my $readable_text = $p->get_readable($text);
DESCRIPTIONThe module is a probability based, corpus-trained tagger that assigns POS 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.
- new %PARAMS
Class constructor. Takes a hash with the following parameters (shown with default
- unknown_word_tag => ''
- Tag to assign to unknown words
- stem => 0
- Stem single words using Lingua::Stem::EN
- weight_noun_phrases => 0
- When returning occurrence counts for a noun phrase, multiply the value by the number of words in the NP.
- longest_noun_phrase => 5
- Will ignore noun phrases longer than this threshold. This affects only the get_words() and get_nouns() methods.
- relax => 0
- Relax the Hidden Markov Model: this may improve accuracy for uncommon words, particularly words used polysemously
- add_tags TEXT
- Examine the string provided and return it fully tagged (XML style)
- get_words TEXT
Given a text string, return as many nouns and
noun phrases as possible. Applies add_tags and involves three stages:
* Tag the text * Extract all the maximal noun phrases * Recursively extract all noun phrases from the MNPs
- get_readable TEXT
- Return an easy-on-the-eyes tagged version of a text string. Applies add_tags and reformats to be easier to read.
- get_sentences TEXT
- Returns an anonymous array of sentences (without POS tags) from a text.
- get_proper_nouns TAGGED_TEXT
- Given a POS-tagged text, this method returns a hash of all proper nouns and their occurrence frequencies. The method is greedy and will return multi-word phrases, if possible, so it would find ``Linguistic Data Consortium'' as a single unit, rather than as three individual proper nouns. This method does not stem the found words.
- get_nouns TAGGED_TEXT
- Given a POS-tagged text, this method returns all nouns and their occurrence frequencies.
- get_max_noun_phrases TAGGED_TEXT
- Given a POS-tagged text, this method returns only the maximal noun phrases. May be called directly, but is also used by get_noun_phrases
- get_noun_phrases TAGGED_TEXT
- Similar to get_words, but requires a POS-tagged text as an argument.
- Reads some included corpus data and saves it in a stored hash on the local file system. This is called automatically if the tagger can't find the stored lexicon.
Aaron Coburn <[email protected]>
Maciej Ceglowski <[email protected]> Eric Nichols, Nara Institute of Science and Technology
COPYRIGHT AND LICENSE
Copyright 2003-2010 Aaron Coburn <[email protected]> This program is free software; you can redistribute it and/or modify it under the terms of version 3 of the GNU General Public License as published by the Free Software Foundation.