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

Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Bandar, Z.A. and McLean, D. 2003. An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering 15 (4) , pp. 871-882. 10.1109/TKDE.2003.1209005

Full text not available from this repository.

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
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1041-4347
Last Modified:	23 Oct 2022 13:10
URI:	https://orca.cardiff.ac.uk/id/eprint/109848

Citation Data

Cited 939 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item