Provided by: libtext-ngram-perl_0.15-2build1_amd64 bug


       Text::Ngram - Ngram analysis of text


         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);


       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:

       This first function returns a hash reference with the n-gram histogram of the text for the
       given window size. The default window size is 5.

           $href = ngram_counts(\%config, $text, $window_size);

       As of version 0.14, the %config may instead be passed in as named arguments:

           $href = ngram_counts($text, $window_size, %config);

       The only necessary parameter is $text.

       The possible value for %config are:


       If set to 1 (default), breaks are flanked by spaces; if set to 0, they're not. Breaks are
       punctuation and other non-alphabetic characters, which, unless you use "punctuation => 0"
       in your configuration, do not make it into the returned hash.

       Here's an example, supposing you're using the default value for punctuation (1):

         my $text = "Hello, world";
         my $hash = ngram_counts($text, 5);

       That produces the following ngrams:

           'Hello' => 1,
           'ello ' => 1,
           ' worl' => 1,
           'world' => 1,

       On the other hand, this:

         my $text = "Hello, world";
         my $hash = ngram_counts({flankbreaks => 0}, $text, 5);

       Produces the following ngrams:

           'Hello' => 1,
           ' worl' => 1,
           'world' => 1,


       If set to 0, casing is preserved. If set to 1, all letters are lowercased before counting
       ngrams. Default is 1.

           # Get all ngrams of size 4 preserving case
           $href_p = ngram_counts( {lowercase => 0}, $text, 4 );


       If set to 0 (default), punctuation is removed before calculating the ngrams.  Set to 1 to
       preserve it.

           # Get all ngrams of size 2 preserving punctuation
           $href_p = ngram_counts( {punctuation => 1}, $text, 2 );


       If set to 0 (default is 1), no ngrams containing spaces will be returned.

          # Get all ngrams of size 3 that do not contain spaces
          $href = ngram_counts( {spaces => 0}, $text, 3);

       If you're going to request both types of ngrams, than the best way to avoid calculating
       the same thing twice is probably this:

           $href_with_spaces = ngram_counts($text[, $window]);
           $href_no_spaces = $href_with_spaces;
           for (keys %$href_no_spaces) { delete $href->{$_} if / / }

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

           add_to_counts($more_text, $window, $href)


       ยท     Look further into the tests. Sort them and add more.


       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

       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.


       Maintained by Alberto Simoes, "".

       Previously maintained by Jose Castro, "".  Originally created by Simon Cozens,


       Copyright 2006 by Alberto Simoes

       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.