NAME

       Lingua::EN::Tagger - Part-of-speech tagger for English natural language processing.

SYNOPSIS

           # 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);

DESCRIPTION

       The 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.

CONSTRUCTOR

       new %PARAMS
           Class constructor.  Takes a hash with the following parameters (shown with default
           values):

           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

METHODS

       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.

       install
           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.

AUTHORS

           Aaron Coburn <aaron@coburncuadrado.com>

CONTRIBUTORS

           Maciej Ceglowski <developer@ceglowski.com>
           Eric Nichols, Nara Institute of Science and Technology

COPYRIGHT AND LICENSE

           Copyright 2003-2010 Aaron Coburn <aaron@coburncuadrado.com>

           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.