/ 
Text-Ngram-0.04
River stage one • 3 direct dependents • 9 total dependents
/ Text::Ngram

NAME

Text::Ngram - Basis for n-gram analysis

SYNOPSIS

use Text::Ngram qw(ngram_counts add_to_counts);
my $text   = "abcdefghijklmnop";
my $hash_r = ngram_counts($text, 3); # Window size = 3
# $hash_r => { abc => 1, bcd => 1, ... }

add_to_counts($more_text, 3, $hash_r);

DESCRIPTION

n-Gram analysis is a field in textual analysis which uses sliding window character sequences in order to aid topic analysis, language determination and so on. The n-gram spectrum of a document can be used to compare and filter documents in multiple languages, prepare word prediction networks, and perform spelling correction.

The neat thing about n-grams, though, is that they're really easy to determine. For n=3, for instance, we compute the n-gram counts like so:

the cat sat on the mat
---                     $counts{"the"}++;
 ---                    $counts{"he "}++;
  ---                   $counts{"e c"}++;
   ...

This module provides an efficient XS-based implementation of n-gram spectrum analysis.

There are two functions which can be imported:

$href = ngram_counts($text[, $window]);

This first function returns a hash reference with the n-gram histogram of the text for the given window size. If the window size is omitted, then 5-grams are used. This seems relatively standard.

add_to_counts($more_text, $window, $href)

This incrementally adds to the supplied hash; if $window is zero or undefined, then the window size is computed from the hash keys.

Important note on text preparation

Most of the published algorithms for textual n-gram analysis assume that the only characters you're interested in are alphabetic characters and spaces. So before the text is counted, the following preparation is made.

All characters are lowercased; (most papers use upper-casing, but that just feels so 1970s) punctuation and numerals are replaced by stop characters flanked by blanks; multiple spaces are compressed into a single space.

After the counts are made, n-grams containing stop characters are dropped from the hash.

If you prefer to do your own text preparation, use the internal routines process_text and process_text_incrementally instead of count_ngrams and add_to_counts respectively.

SEE ALSO

Cavnar, W. B. (1993). N-gram-based text filtering for TREC-2. In D. Harman (Ed.), Proceedings of TREC-2: Text Retrieval Conference 2. Washington, DC: National Bureau of Standards.

Shannon, C. E. (1951). Predication and entropy of printed English. The Bell System Technical Journal, 30. 50-64.

Ullmann, J. R. (1977). Binary n-gram technique for automatic correction of substitution, deletion, insert and reversal errors in words. Computer Journal, 20. 141-147.

AUTHOR

Maintained by Jose Castro, cog@cpan.org. Originally created by Simon Cozens, simon@cpan.org.

COPYRIGHT AND LICENSE

Copyright 2004 by Jose Castro Copyright 2003 by Simon Cozens

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.