MALLET is a Java-based toolkit for statistical natural language processing, document classification, clustering, topic modeling, and information extraction.
MALLET is an open-source, Java-based software package designed for various machine learning applications to text. It provides a comprehensive suite of tools for statistical natural language processing, including functionalities for document classification, text clustering, and information extraction. The toolkit is particularly strong in document classification, offering efficient routines for text-to-feature conversion, a variety of algorithms like Naïve Bayes and Maximum Entropy, and methods for evaluating classifier performance. Beyond classification, MALLET includes tools for sequence tagging, useful for tasks such as named-entity extraction, with algorithms like Hidden Markov Models and Conditional Random Fields. It also features an efficient topic modeling toolkit with sampling-based implementations of Latent Dirichlet Allocation and Hierarchical LDA for analyzing large collections of unlabeled text. The package incorporates efficient numerical optimization methods, including Limited Memory BFGS, and flexible routines for transforming text documents into numerical representations through a system of 'pipes' for tasks like tokenization and stopword removal. An add-on, GRMM, supports inference in general graphical models.
Platforms: Linux, macOS, Windows
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