AI-Categorizer
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lib/AI/Categorizer/Document.pm view on Meta::CPAN
my ($self, %args) = @_;
my $path = $args{path} or die "No 'path' argument given to dump_features()";
open my($fh), "> $path" or die "Can't create $path: $!";
my $f = $self->features->as_hash;
while (my ($k, $v) = each %$f) {
print $fh "$k\t$v\n";
}
}
1;
__END__
=head1 NAME
AI::Categorizer::Document - Embodies a document
=head1 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);
=head1 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.
=head1 METHODS
=over 4
=item 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 C<new()> in the latter case, since the KnowledgeSet or
Collection classes will create Document objects for you in the former
cases.
The C<new()> method accepts the following parameters:
=over 4
=item name
A string that identifies this document. Required.
=item content
The raw content of this document. May be specified as either a string
or as a hash reference, allowing structured document types.
=item 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 C<subject> section has a weight of 3 and a
C<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
C<subject> appeared 3 times.
If no weights are specified, all weights are set to 1.
=item 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.
=item categories
A reference to an array of Category objects that this document belongs
to. Optional.
=item 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.
=item use_features
A Feature Vector specifying the only features that should be
considered when parsing this document. This is an alternative to
using C<stopwords>.
=item stemming
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