Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
A maximum entropy approach to natural language processing
Computational Linguistics
An Introduction to Variational Methods for Graphical Models
Machine Learning
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
HLT '93 Proceedings of the workshop on Human Language Technology
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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This paper describes an exponential family model of word sense which captures both occurrences and co-occurrences of words and senses in a joint probability distribution. This statistical framework lends itself to the task of word sense disambiguation. We evaluate the performance of the model in its participation on the SemEval-2007 coarse- and fine-grained all-words tasks under a variety of parameters.