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An approach for measuring semantic similarity between words using multiple information sources

Li, Yuhua and Bandar, Zuhair A. and McLean, David A. (2003) An approach for measuring semantic similarity between words using multiple information sources. ISSN 1041-4347

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Abstract

Semantic similarity between words is becoming a generic problem for many applications of computational linguistics and artificial intelligence. This paper explores the determination of semantic similarity by a number of information sources, which consist of structural semantic information from a lexical taxonomy and information content from a corpus. To investigate how information sources could be used effectively, a variety of strategies for using various possible information sources are implemented. A new measure is then proposed which combines information sources nonlinearly. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed measure significantly outperforms traditional similarity measures.

Item Type: Article
Additional Information: Full-text of this article is not available in this e-prints service. This article was originally published following peer-review in IEEE Transactions on Knowledge and Data Engineering, published by and copyright IEEE.
Divisions: Legacy Research Institutes > Dalton Research Institute > Computer Science
Date Deposited: 16 Sep 2009 13:07
Last Modified: 01 Sep 2016 13:56
URI: http://e-space.mmu.ac.uk/id/eprint/81276

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