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

2.50
Hdl Handle:
http://hdl.handle.net/2173/81276
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

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Yuhua-
dc.contributor.authorBandar, Zuhair A.-
dc.contributor.authorMcLean, David A.-
dc.date.accessioned2009-09-16T13:07:29Z-
dc.date.available2009-09-16T13:07:29Z-
dc.date.issued2003-
dc.identifier.citationIEEE transactions on knowledge and data engineering, 2003, vol. 15, no. 4, pp. 871-882en
dc.identifier.issn1041-4347-
dc.identifier.doi10.1109/TKDE.2003.1209005-
dc.identifier.urihttp://hdl.handle.net/2173/81276-
dc.descriptionFull-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.en
dc.description.abstractSemantic 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.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://www2.computer.org/portal/web/tkdeen
dc.titleAn approach for measuring semantic similarity between words using multiple information sourcesen
dc.typeArticleen
dc.identifier.eissn1558-2191-
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