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A statistical model for near-synonym choice

A statistical model for near-synonym choice We present an unsupervised statistical method for automatic choice of near-synonyms when the context is given. The method uses the Web as a corpus to compute scores based on mutual information. Our evaluation experiments show that this method performs better than two previous methods on the same task. We also describe experiments in using supervised learning for this task. We present an application to an intelligent thesaurus. This work is also useful in machine translation and natural language generation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

A statistical model for near-synonym choice

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Knowledge acquisition
ISSN
1550-4875
DOI
10.1145/1187415.1187417
Publisher site
See Article on Publisher Site

Abstract

We present an unsupervised statistical method for automatic choice of near-synonyms when the context is given. The method uses the Web as a corpus to compute scores based on mutual information. Our evaluation experiments show that this method performs better than two previous methods on the same task. We also describe experiments in using supervised learning for this task. We present an application to an intelligent thesaurus. This work is also useful in machine translation and natural language generation.

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: Jan 1, 2007

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