AI::Categorizer::Document - Embodies a document

 use AI::Categorizer::Document;

 # Simplest way to create a document:
 my $d = new AI::Categorizer::Document(name => $string,
                                       content => $string);

 # Other parameters are accepted:
 my $d = new AI::Categorizer::Document(name => $string,
                                       categories => \@category_objects,
                                       content => { subject => $string,
                                                    body => $string2, ... },
                                       content_weights => { subject => 3,
                                                            body => 1, ... },
                                       stopwords => \%skip_these_words,
                                       stemming => $string,
                                       front_bias => $float,
                                       use_features => $feature_vector,
                                      );

 # Specify explicit feature vector:
 my $d = new AI::Categorizer::Document(name => $string);
 $d->features( $feature_vector );

 # Now pass the document to a categorization algorithm:
 my $learner = AI::Categorizer::Learner::NaiveBayes->restore_state($path);
 my $hypothesis = $learner->categorize($document);

The Document class embodies the data in a single document, and contains methods for turning this data into a FeatureVector. Usually documents are plain text, but subclasses of the Document class may handle any kind of data.

new(%parameters)

Creates a new Document object. Document objects are used during training (for the training documents), testing (for the test documents), and when categorizing new unseen documents in an application (for the unseen documents). However, you'll typically only call new() in the latter case, since the KnowledgeSet or Collection classes will create Document objects for you in the former cases.

The new() method accepts the following parameters:

name

A string that identifies this document. Required.

content

The raw content of this document. May be specified as either a string or as a hash reference, allowing structured document types.

content_weights

A hash reference indicating the weights that should be assigned to features in different sections of a structured document when creating its feature vector. The weight is a multiplier of the feature vector values. For instance, if a subject section has a weight of 3 and a body section has a weight of 1, and word counts are used as feature vector values, then it will be as if all words appearing in the subject appeared 3 times.

If no weights are specified, all weights are set to 1.

front_bias

Allows smooth bias of the weights of words in a document according to their position. The value should be a number between -1 and 1. Positive numbers indicate that words toward the beginning of the document should have higher weight than words toward the end of the document. Negative numbers indicate the opposite. A bias of 0 indicates that no biasing should be done.

categories

A reference to an array of Category objects that this document belongs to. Optional.

stopwords

A list/hash of features (words) that should be ignored when parsing document content. A hash reference is preferred, with the features as the keys. If you pass an array reference containing the features, it will be converted to a hash reference internally.

use_features

A Feature Vector specifying the only features that should be considered when parsing this document. This is an alternative to using stopwords.

stemming

Indicates the linguistic procedure that should be used to convert tokens in the document to features. Possible values are none, which indicates that the tokens should be used without change, or porter, indicating that the Porter stemming algorithm should be applied to each token. This requires the Lingua::Stem module from CPAN.

stopword_behavior

There are a few ways you might want the stopword list (specified with the stopwords parameter) to interact with the stemming algorithm (specified with the stemming parameter). These options can be controlled with the stopword_behavior parameter, which can take the following values:

no_stem

Match stopwords against non-stemmed document words.

stem

Stem stopwords according to 'stemming' parameter, then match them against stemmed document words.

pre_stemmed

Stopwords are already stemmed, match them against stemmed document words.

The default value is stem, which seems to produce the best results in most cases I've tried. I'm not aware of any studies comparing the no_stem behavior to the stem behavior in the general case.

This parameter has no effect if there are no stopwords being used, or if stemming is not being used. In the latter case, the list of stopwords will always be matched as-is against the document words.

Note that if the stem option is used, the data structure passed as the stopwords parameter will be modified in-place to contain the stemmed versions of the stopwords supplied.

read( path => $path )

An alternative constructor method which reads a file on disk and returns a document with that file's contents.

parse( content => $content )

name()

Returns this document's name property as specified when the document was created.

features()

Returns the Feature Vector associated with this document.

categories()

In a list context, returns a list of Category objects to which this document belongs. In a scalar context, returns the number of such categories.

create_feature_vector()

Creates this document's Feature Vector by parsing its content. You won't call this method directly, it's called by new().

Ken Williams <ken@mathforum.org>

This distribution is free software; you can redistribute it and/or modify it under the same terms as Perl itself. These terms apply to every file in the distribution - if you have questions, please contact the author.