NAME AI::Categorizer - Automatic Text Categorization SYNOPSIS use AI::Categorizer; my $c = new AI::Categorizer(...parameters...); # Run a complete experiment - training on a corpus, testing on a test # set, printing a summary of results to STDOUT $c->run_experiment; # Or, run the parts of $c->run_experiment separately $c->scan_features; $c->read_training_set; $c->train; $c->evaluate_test_set; print $c->stats_table; # After training, use the Learner for categorization my $l = $c->learner; while (...) { my $d = ...create a document... my $hypothesis = $l->categorize($d); # An AI::Categorizer::Hypothesis object print "Assigned categories: ", join ', ', $hypothesis->categories, "\n"; print "Best category: ", $hypothesis->best_category, "\n"; } DESCRIPTION "AI::Categorizer" is a framework for automatic text categorization. It consists of a collection of Perl modules that implement common categorization tasks, and a set of defined relationships among those modules. The various details are flexible - for example, you can choose what categorization algorithm to use, what features (words or otherwise) of the documents should be used (or how to automatically choose these features), what format the documents are in, and so on. The basic process of using this module will typically involve obtaining a collection of pre-categorized documents, creating a knowledge set representation of those documents, training a categorizer on that knowledge set, and saving the trained categorizer for later use. There are several ways to carry out this process. The top-level "AI::Categorizer" module provides an umbrella class for high-level operations, or you may use the interfaces of the individual classes in the framework. Disclaimer: the results of any of the machine learning algorithms are far from infallible (close to fallible?). Categorization of documents is often a difficult task even for humans well-trained in the particular domain of knowledge, and there are many things a human would consider that none of these algorithms consider. These are only statistical tests - at best they are neat tricks or helpful assistants, and at worst they are totally unreliable. If you plan to use this module for anything important, human supervision is essential, both of the categorization process and the final results. For the usage details, please see the documentation of each individual module. FRAMEWORK COMPONENTS This section explains the major pieces of the "AI::Categorizer" object framework. This section gives a conceptual overview, but does not get into any of the details about interfaces or usage. See the documentation for the individual classes for more details. A diagram of the various classes in the framework can be seen in "doc/classes.png". Knowledge Sets A "knowledge set" is defined as a collection of documents, stored in a particular format, together with some information on the categories each document belongs to. Note that this term is somewhat unique to this project - other sources may call it a "training corpus", or "prior knowledge". A knowledge set also contains some information on how documents will be parsed and how their features (words) will be extracted and culled. In this sense, a knowledge set represents not only a collection of data, but a particular view on that data. A knowledge set is encapsulated by the "AI::Categorizer::KnowledgeSet" class. Before you can start playing with categorizers, you will have to start playing with knowledge sets, so that the categorizers have some data to train on. See the documentation for the "AI::Categorizer::KnowledgeSet" module for information on its interface. Feature selection Deciding which features are the most important is a very large part of the categorization task - you cannot simply consider all the words in all the documents when training, and all the words in the document being categorized. There are two main reasons for this - first, it would mean that your training and categorizing processes would take forever and use tons of memory, and second, the significant bits of the documents would get lost in the "noise" of the insignificant bits. The process of selecting the most important features in the training set is called "feature selection". It is managed by the "AI::Categorizer::KnowledgeSet" class, and you will find the details of feature selection processes in that class's documentation. Collections Because documents may be stored in lots of different formats, a *Collection* class has been created as an abstraction of a stored set of documents, together with a way to iterate through the set and return Document objects. A "KnowledgeSet" contains a single collection object. A "Categorizer" generally contains two collections, one for training and one for testing. A "Learner" can mass-categorize a collection. Categorization Algorithms Each categorization algorithm is a subclass of "AI::Categorizer::Learner". Currently the framework only includes one categorizer in its default distribution, "AI::Categorizer::Learner::NaiveBayes". There will soon be a Neural Network categorizer. Next on the agenda will/may be a k-Nearest-Neighbor algorithm, a decision tree algorithm, a mixture-of-experts combiner, and/or a general interface to the "Weka" machine learning system. No timetable for their creation has yet been set. Please see the documentation of these individual modules for more details on their guts and quirks. See the "AI::Categorizer::Learner" documentation for a description of the general categorizer interface. Feature Vectors Most categorization algorithms don't deal directly with a document's data, they instead deal with a *vector representation* of a document's *features*. The features may be any properties of the document that seem indicative of its category, but they are usually some version of the "most important" words in the document. A list of features and their weights in each document is encapsulated by the "AI::Categorizer::FeatureVector" class. You may think of this class as roughly analogous to a Perl hash, where the keys are the names of features and the values are their weights. Hypotheses The result of asking a categorizer to categorize a previously unseen document is called a hypothesis, because it is some kind of "statistical guess" of what categories this document should be assigned to. Since you may be interested in any of several pieces of information about the hypothesis (for instance, which categories were assigned, which category was the single most likely category, the scores assigned to each category, etc.), the hypothesis is returned as an object of the "AI::Categorizer::Hypothesis" class, and you can use its object methods to get information about the hypothesis. See its class documentation for the details. Experiments The "AI::Categorizer::Experiment" class helps you organize the results of categorization experiments. As you get lots of categorization results (Hypotheses) back from the Learner, you can feed these results to the Experiment class, along with the correct answers. When all results have been collected, you can get a report on accuracy, precision, recall, F1, and so on, with both micro-averaging and macro-averaging over categories. See the docs for "AI::Categorizer::Experiment" for more details. METHODS new() Creates a new Categorizer object and returns it. Accepts lots of parameters controlling behavior. In addition to the parameters listed here, you may pass any parameter accepted by any class that we create internally (the KnowledgeSet, Learner, Experiment, or Collection classes). This is managed by the "Class::Container" module, so see its documentation for the details of how this works. The specific parameters accepted here are: progress_file A string that indicates a place where objects will be saved during several of the methods of this class. The default value is the string "save", which means files like "save-01-knowledge_set" will get created. The exact names of these files may change in future releases, since they're just used internally to resume where we last left off. verbose If true, a few status messages will be printed during execution. data_root A shortcut for setting the "training_set", "test_set", and "category_file" parameters separately. Sets "training_set" to "$data_root/training", "test_set" to "$data_root/test", and "category_file" (used by some of the Collection classes) to "$data_root/cats.txt". training_set Specifies the "path" parameter that will be fed to the KnowledgeSet's "scan_features()" and "read()" methods during our "scan_features()" and "read_training_set()" methods. test_set Specifies the "path" parameter that will be used when creating a Collection during the "evaluate_test_set()" method. stopword_file Specifies a file containing a list of "stopwords", which are words that should automatically be disregarded when scanning/reading documents. The file should contain one word per line. The file will be parsed and then fed as the "stopwords" parameter to the KnowledgeSet "new()" method. learner() Returns the Learner object associated with this Categorizer. If "learner()" is called before "train()", the Learner will of course not be trained yet. knowledge_set() Returns the KnowledgeSet object associated with this Categorizer. If "read_training_set()" has not yet been called, the KnowledgeSet will not yet be populated with any training data. run_experiment() Runs a complete experiment on the training and testing data, reporting the results on "STDOUT". Internally, this is just a shortcut for calling the "scan_features()", "read_training_set()", "train()", and "evaluate_test_set()" methods, then printing the value of the "stats_table()" method. scan_features() Scans the Collection specified in the "test_set" parameter to determine the set of features (words) that will be considered when training the Learner. Internally, this calls the "scan_features()" method of the KnowledgeSet, then saves the KnowledgeSet for later use. This step is not strictly necessary, but it can dramatically reduce memory requirements if you scan for features before reading the entire corpus into memory. read_training_set() Populates the KnowledgeSet with the data specified in the "test_set" parameter. Internally, this call the "read()" method of the KnowledgeSet. Returns the KnowledgeSet. Also saves the KnowledgeSet object for later use. train() Calls the Learner's "train()" method, passing it the KnowledgeSet populated during "read_training_set()". Returns the Learner object. Also save the Learner object for later use. evaluate_test_set() Creates a Collection based on the value of the "test_set" parameter, and calls the Learner's "categorize_collection()" method using this Collection. Returns the resultant Experiment object. Also saves the Experiment object for later use in the "stats_table()" method. stats_table() Returns the value of the Experiment's (as created by "evaluate_test_set()") "stats_table()" method. This is a string that shows various statistics about the accuracy/precision/recall/F1/etc. of the assignments made during testing. HISTORY This module is a revised and redesigned version of the previous "AI::Categorize" module by the same author. Note the added 'r' in the new name. The older module has a different interface, and no attempt at backward compatibility has been made - that's why I changed the name. You can have both "AI::Categorize" and "AI::Categorizer" installed at the same time on the same machine, if you want. They don't know about each other or use conflicting namespaces. AUTHOR Ken Williams REFERENCES http://www.d.umn.edu/~tpederse/nsp.html (could be used later for feature selection) COPYRIGHT 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.