Mallet provides a range of machine learning capabilities applied to statistical natural language processing mechanisms, including document classification, clustering, and information extraction.
Overview
MALLET provides a range of machine learning capabilities applied to statistical natural-language processing mechanisms, including document classification, clustering, and information extraction. MALLET was developed by researchers at the University of Massachusetts partially with DARPA PAL funding. The MALLET website provides all supporting information at http://mallet.cs.umass.edu/.
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 with several 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 (Broyden–Fletcher–Goldfarb–Shanno), 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.
Prerequisites
- Java 1.5 or later
- Sample Data for MALLET training
Overview: DISTAR 14982 – Approved for Public Release, Distribution Unlimited