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.

MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. [Quick Start] [Developer's Guide]

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. [Quick Start] [Developer's Guide]

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. [Quick Start]

Many of the algorithms in MALLET depend on numerical optimization. MALLET includes an efficient implementation of Limited Memory BFGS, among many other optimization methods. [Developer's Guide]

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. [Quick Start] [Developer's Guide]

An add-on package to MALLET, called GRMM, contains support for inference in general graphical models, and training of CRFs with arbitrary graphical structure. [About GRMM]

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  McCallum, Andrew Kachites.  "MALLET: A Machine Learning for Language Toolkit."
    http://mallet.cs.umass.edu. 2002.