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


    # 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);


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.



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


add_tags TEXT

Examine the string provided and return it fully tagged (XML style)

add_tags_incrementally TEXT

Examine the string provided and return it fully tagged (XML style) but do not reset the internal part-of-speech state between invocations.

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 <>


    Maciej Ceglowski <>
    Eric Nichols, Nara Institute of Science and Technology


    Copyright 2003-2010 Aaron Coburn <>

    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.