MALLET: A Machine Learning for Language Toolkit. McCallum, A., editor 2002. ![MALLET: A Machine Learning for Language Toolkit. [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.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.
@article{mccallum_mallet:_2002,
title = {{MALLET}: {A} {Machine} {Learning} for {Language} {Toolkit}.},
shorttitle = {{MALLET}},
url = {http://mallet.cs.umass.edu/index.php/Main_Page},
abstract = {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.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.},
urldate = {2017-12-26TZ},
editor = {McCallum, Andrew},
year = {2002},
keywords = {machine-learning, natural-language-processing, open-source-software\_and\_tools, topic-modeling}
}
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