Lingua::EN::Bigram (version 0.01)
Tuesday, June 23rd, 2009Below is the POD (Plain O’ Documentation) file describing a Perl module I wrote called Lingua::EN::Bigram.
The purpose of the module is to: 1) extract all of the two-word phrases from a given text, and 2) rank each phrase according to its probability of occurance. Very nice for doing textual analysis. For example, by applying this module to Mark Twain’s Adventures of Tom Sawyer it becomes evident that the signifcant two-word phrases are names of characters in the story. On the other hand, Ralph Waldo Emerson’s Essays: First Series returns action statements — instructions. On the other hand Henry David Thoreau’s Walden returns “walden pond” and descriptions of pine trees. Interesting.
The code is available here or on CPAN.
NAME
Lingua::EN::Bigram – Calculate significant two-word phrases based on frequency and/or T-Score
SYNOPSIS
use Lingua::EN::Bigram;
$bigram = Lingua::EN::Bigram->new;
$bigram->text( 'All men by nature desire to know. An indication of this...' );
$tscore = $bigram->tscore;
foreach ( sort { $$tscore{ $b } <=> $$tscore{ $a } } keys %$tscore ) {
print "$$tscore{ $_ }\t" . "$_\n";
}
DESCRIPTION
This module is designed to: 1) pull out all of the two-word phrases (collocations or “bigrams”) in a given text, and 2) list these phrases according to thier frequency and/or T-Score. Using this module is it possible to create list of the most common two-word phrases in a text as well as order them by their probable occurance, thus implying significance.
METHODS
new
Create a new, empty bigram object:
# initalize
$bigram = Lingua::EN::Bigram->new;
text
Set or get the text to be analyzed:
# set the attribute
$bigram->text( 'All good things must come to an end...' );
# get the attribute
$text = $bigram->text;
words
Return a list of all the tokens in a text. Each token will be a word or puncutation mark:
# get words
@words = $bigram->words;
word_count
Return a reference to a hash whose keys are a token and whose values are the number of times the token occurs in the text:
# get word count
$word_count = $bigram->word_count;
# list the words according to frequency
foreach ( sort { $$word_count{ $b } <=> $$word_count{ $a } } keys %$word_count ) {
print $$word_count{ $_ }, "\t$_\n";
}
bigrams
Return a list of all bigrams in the text. Each item will be a pair of tokens and the tokens may consist of words or puncutation marks:
# get bigrams
@bigrams = $bigram->bigrams;
bigram_count
Return a reference to a hash whose keys are a bigram and whose values are the frequency of the bigram in the text:
# get bigram count
$bigram_count = $bigram->bigram_count;
# list the bigrams according to frequency
foreach ( sort { $$bigram_count{ $b } <=> $$bigram_count{ $a } } keys %$bigram_count ) {
print $$bigram_count{ $_ }, "\t$_\n";
}
tscore
Return a reference to a hash whose keys are a bigram and whose values are a T-Score — a probabalistic calculation determining the significance of bigram occuring in the text:
# get t-score
$tscore = $bigram->tscore;
# list bigrams according to t-score
foreach ( sort { $$tscore{ $b } <=> $$tscore{ $a } } keys %$tscore ) {
print "$$tscore{ $_ }\t" . "$_\n";
}
DISCUSSION
Given the increasing availability of full text materials, this module is intended to help “digital humanists” apply mathematical methods to the analysis of texts. For example, the developer can extract the high-frequency words using the word_count method and allow the user to search for those words in a concordance. The bigram_count method simply returns the frequency of a given bigram, but the tscore method can order them in a more finely tuned manner.
Consider using T-Score-weighted bigrams as classification terms to supplement the “aboutness” of texts. Concatonate many texts together and look for common phrases written by the author. Compare these commonly used phrases to the commonly used phrases of other authors.
Each bigram includes punctuation. This is intentional. Developers may need want to remove bigrams containing such values from the output. Similarly, no effort has been made to remove commonly used words — stop words — from the methods. Consider the use of Lingua::StopWords, Lingua::EN::StopWords, or the creation of your own stop word list to make output more meaningful. The distribution came with a script (bin/bigrams.pl) demonstrating how to remove puncutation and stop words from the displayed output.
Finally, this is not the only module supporting bigram extraction. See also Text::NSP which supports n-gram extraction.
TODO
There are probably a number of ways the module can be improved:
- the constructor method could take a scalar as input, thus reducing the need for the text method
- the distribution’s license should probably be changed to the Perl Aristic License
- the addition of alternative T-Score calculations would be nice
- it would be nice to support n-grams
- make sure the module works with character sets beyond ASCII
ACKNOWLEDGEMENTS
T-Score is calculated as per Nugues, P. M. (2006). An introduction to language processing with Perl and Prolog: An outline of theories, implementation, and application with special consideration of English, French, and German. Cognitive technologies. Berlin: Springer. Page 109.
AUTHOR
Eric Lease Morgan <eric_morgan@infomotions.com>