Naive mixes for word sense disambiguation

Ted Pedersen

Naive mixes for word sense disambiguation

Computer Science (Duluth)

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the National Conference on Artificial Intelligence
Editors	Anon
Publisher	AAAI
Number of pages	1
State	Published - Dec 1 1997
Event	Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA Duration: Jul 27 1997 → Jul 31 1997

Other

Other	Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97
City	Providence, RI, USA
Period	7/27/97 → 7/31/97

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title = "Naive mixes for word sense disambiguation",

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.",

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AB - 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.

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M3 - Conference contribution

AN - SCOPUS:0031358385

BT - Proceedings of the National Conference on Artificial Intelligence

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T2 - Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97

Y2 - 27 July 1997 through 31 July 1997

ER -

Naive mixes for word sense disambiguation

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