Naive mixes for word sense disambiguation

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

Abstract

The Naive Mix is a new supervised learning algorithm on sequential model selection. The algorithm combines models discarded during the selection process with the best-fitting model to form an averaged probabilistic model. This improves classification accuracy when applied to the problem of determining the meaning of an ambiguous word in a sentence. Experimental results disambiguating four nouns, four verbs, and four adjectives show that it is competitive with a variety of machine learning algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Editors Anon
PublisherAAAI
Number of pages1
StatePublished - Dec 1 1997
EventProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA
Duration: Jul 27 1997Jul 31 1997

Other

OtherProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97
CityProvidence, RI, USA
Period7/27/977/31/97

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