Unix Man page/Perldoc/Info page, English-Chinese Dictionary,
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Unix Man page/Perldoc/Info page, English-Chinese Dictionary,
Chinese-English Dictionary
AI::Categorizer::DocumUser3Contributed Perl DocuAI::Categorizer::Document(3pm) NAME AI::Categorizer::Document - Embodies a document SYNOPSIS 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); DESCRIPTION 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. METHODS new(%parameters) Creates a new Document object. Document objects are used during training (for the training documents), testing (for the test docu- ments), and when categorizing new unseen documents in an applica- tion (for the unseen documents). However, you'll typically only call "new()" in the latter case, since the KnowledgeSet or Collec- tion 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 indi- cate 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 con- taining 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 con- vert 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 algo- rithm should be applied to each token. This requires the "Lin- gua::Stem" module from CPAN. stopword_behavior There are a few ways you might want the stopword list (speci- fied with the "stopwords" parameter) to interact with the stem- ming 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 docu- ment 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()". AUTHOR Ken Williams <ken AT mathforum.org> COPYRIGHT This distribution is free software; you can redistribute it and/or mod- ify 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. perl v5.8.7 2002-11-24 AI::Categorizer::Document(3pm) |