AI::Categorizer - Automatic Text Categorization


 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
 # Or, run the parts of $c->run_experiment separately
 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";


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.

A simple sample script that reads a training corpus, trains a categorizer, and tests the categorizer on a test corpus, is distributed as eg/ .

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 really 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.


This section explains the major pieces of the AI::Categorizer object framework. We give a conceptual overview, but don't 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-overview.png, and a more detailed view of the same thing can be seen in doc/classes.png.

Knowledge Sets

A "knowledge set" is defined as a collection of documents, 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 turned into meaningful representations. 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 stuff of the documents would get lost in the "noise" of the insignificant stuff.

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.


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 knowledge set contains a single collection object. A Categorizer doing a complete test run generally contains two collections, one for training and one for testing. A Learner can mass-categorize a collection.

The AI::Categorizer::Collection class and its subclasses instantiate the idea of a collection in this sense.


Each document is represented by an AI::Categorizer::Document object, or an object of one of its subclasses. Each document class contains methods for turning a bunch of data into a Feature Vector. Each document also has a method to report which categories it belongs to.


Each category is represented by an AI::Categorizer::Category object. Its main purpose is to keep track of which documents belong to it, though you can also examine statistical properties of an entire category, such as obtaining a Feature Vector representing an amalgamation of all the documents that belong to it.

Machine Learning Algorithms

There are lots of different ways to make the inductive leap from the training documents to unseen documents. The Machine Learning community has studied many algorithms for this purpose. To allow flexibility in choosing and configuring categorization algorithms, each such algorithm is a subclass of AI::Categorizer::Learner. There are currently four categorizers included in the distribution:


A pure-perl implementation of a Naive Bayes classifier. No dependencies on external modules or other resources. Naive Bayes is usually very fast to train and fast to make categorization decisions, but isn't always the most accurate categorizer.


An interface to Corey Spencer's Algorithm::SVM, which implements a Support Vector Machine classifier. SVMs can take a while to train (though in certain conditions there are optimizations to make them quite fast), but are pretty quick to categorize. They often have very good accuracy.


An interface to AI::DecisionTree, which implements a Decision Tree classifier. Decision Trees generally take longer to train than Naive Bayes or SVM classifiers, but they are also quite fast when categorizing. Decision Trees have the advantage that you can scrutinize the structures of trained decision trees to see how decisions are being made.


An interface to version 2 of the Weka Knowledge Analysis system that lets you use any of the machine learners it defines. This gives you access to lots and lots of machine learning algorithms in use by machine learning researches. The main drawback is that Weka tends to be quite slow and use a lot of memory, and the current interface between Weka and AI::Categorizer is a bit clumsy.

Other machine learning methods that may be implemented soonish include Neural Networks, k-Nearest-Neighbor, and/or a mixture-of-experts combiner for ensemble learning. 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.

If you wish to create your own classifier, you should inherit from AI::Categorizer::Learner or AI::Categorizer::Learner::Boolean, which are abstract classes that manage some of the work for you.

Feature Vectors

Most categorization algorithms don't deal directly with documents' data, they instead deal with a vector representation of a document's features. The features may be any properties of the document that seem helpful for determining 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.


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.


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. We use the Statistics::Contingency module from CPAN to manage the calculations. See the docs for AI::Categorizer::Experiment for more details.



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), or any class that they create. 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:


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.


If true, a few status messages will be printed during execution.


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.


Specifies the path parameter that will be used when creating a Collection during the evaluate_test_set() method.


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.


Returns the Learner object associated with this Categorizer. Before train(), the Learner will of course not be trained yet.


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.


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.


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 a list of the KnowledgeSet's features 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.


Populates the KnowledgeSet with the data specified in the test_set parameter. Internally, this calls the read() method of the KnowledgeSet. Returns the KnowledgeSet. Also saves the KnowledgeSet object for later use.


Calls the Learner's train() method, passing it the KnowledgeSet created during read_training_set(). Returns the Learner object. Also saves the Learner object for later use.


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.


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.


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.


Ken Williams <>

Discussion about this module can be directed to the perl-AI list at <>. For more info about the list, see


An excellent introduction to the academic field of Text Categorization is Fabrizio Sebastiani's "Machine Learning in Automated Text Categorization": ACM Computing Surveys, Vol. 34, No. 1, March 2002, pp. 1-47.


Copyright 2000-2003 Ken Williams. All rights reserved.

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.