Description:

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for document classification: efficient routines for converting text to "features", algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using commonly used metrics.

In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers.

Topic models are useful for analyzing large collections of unlabeled text. The MALLET topic modeling toolkit contains efficient, sampling-based implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA.

Many of the algorithms in MALLET depend on numerical optimization. MALLET includes an efficient implementation of Limited Memory BFGS, among many other optimization methods.

In addition to sophisticated Machine Learning applications, MALLET includes routines for transforming text documents into numerical representations that can then be processed efficiently. This process is implemented through a flexible system of "pipes", which handle distinct tasks such as tokenizing strings, removing stopwords, and converting sequences into count vectors.

Changes to previous version:

MALLET 2.0 RC4 Release Notes July 16, 2009

Major updates:

An implementation of generalized expectation criteria training of MaxEnt classifiers and methods for obtaining constraints (c.f. Gregory Druck, Gideon Mann, Andrew McCallum "Learning from Labeled Features using Generalized Expectation Criteria.")

PagedInstanceList has been substantially rewritten by Mike Bond.

Bug fixes to topic model hyperparameter optimization and topic inference.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Sequence Analysis, Classification, Clustering, Topic Modeling, Information Extraction

Archive: download here

Other available revisons

Version	Changelog	Date
2.0-rc4	MALLET 2.0 RC4 Release Notes July 16, 2009 Major updates: An implementation of generalized expectation criteria training of MaxEnt classifiers and methods for obtaining constraints (c.f. Gregory Druck, Gideon Mann, Andrew McCallum "Learning from Labeled Features using Generalized Expectation Criteria.") PagedInstanceList has been substantially rewritten by Mike Bond. Bug fixes to topic model hyperparameter optimization and topic inference.	August 24, 2009, 23:10:14
2.0-rc3	MALLET 2.0 RC3 Release Notes Mar 31, 2009 Internationalization in "import-file" command: support for character encodings, more flexible tokenization patterns, and user-supplied stoplists. Options for L1 regularization for CRFs and MaxEnt classifiers. For classifiers, use the option "--trainer MaxEnt,l1Weight=1.0". Topic modeling: new multi-threaded option, faster sampling with less memory, two new formats for saving topic-word weights to files, topic inference for unseen documents. Removed obsolete ant, lucene and junit jars, updated to JUnit 4. Many bug fixes.	April 2, 2009, 18:05:07
2.0-rc1	Initial Announcement on mloss.org.	October 3, 2008, 00:10:23

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