Knowledge lean word-sense disambiguation

Ted Pedersen, Rebecca Bruce

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations

Abstract

We present a corpus-based approach to word-sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniques to estimate the parameters of a model describing the conditional distribution of the sense group given the known contextual features. Both the EM algorithm and Gibbs Sampling are evaluated to determine which is most appropriate for our data. We compare their disambiguation accuracy in an experiment with thirteen different words and three feature sets. Gibbs Sampling results in small but consistent improvement in disambiguation accuracy over the EM algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
PublisherAAAI
Pages800-805
Number of pages6
StatePublished - Jan 1 1998
EventProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA
Duration: Jul 26 1998Jul 30 1998

Other

OtherProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
CityMadison, WI, USA
Period7/26/987/30/98

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    Pedersen, T., & Bruce, R. (1998). Knowledge lean word-sense disambiguation. In Anon (Ed.), Proceedings of the National Conference on Artificial Intelligence (pp. 800-805). AAAI.