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espace at MMU > Research Institutes > Dalton Research Institute > Computer Science > An approach for measuring semantic similarity between words using multiple information sources

Please use this identifier to cite or link to this item: http://hdl.handle.net/2173/81276
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Title: An approach for measuring semantic similarity between words using multiple information sources
Authors: Li, Yuhua
Bandar, Zuhair A.
McLean, David A.
Citation: IEEE transactions on knowledge and data engineering, 2003, vol. 15, no. 4, pp. 871-882
Publisher: IEEE
Issue Date: 2003
URI: http://hdl.handle.net/2173/81276
DOI: 10.1109/TKDE.2003.1209005
Additional Links: http://www2.computer.org/portal/web/tkde
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.
Type: Article
Language: en
Description: 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.
ISSN: 1041-4347
EISSN: 1558-2191
Appears in Collections: Computer Science

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