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Measuring semantic similarity between words using lexical knowledge and neural networks

Li, Y, Bandar, Z and Mclean, D 2002, 'Measuring semantic similarity between words using lexical knowledge and neural networks' , in: Intelligent Data Engineering and Automated Learning — IDEAL 2002 : Third International Conference Manchester, UK, August 12–14, 2002 Proceedings , Lecture Notes in Computer Science (2412) , Springer Berlin Heidelberg, pp. 111-116.

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This paper investigates the determination of semantic similarity by the incorporation of structural semantic knowledge from a lexical database and the learning ability of neural networks. The lexical database is assumed to be organised in a hierarchical structure. The extracted lexical knowledge contains the relative location of the concerned words in the lexical hierarchy. The neural network then processes available lexical knowledge to provide semantic similarity for words. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed method is effective in measuring semantic similarity between words.

Item Type: Book Section
Editors: Yin, H, Allinson, N, Freeman, R, Keane, J and Hubbard, S
Schools: Schools > School of Computing, Science and Engineering
Publisher: Springer Berlin Heidelberg
Refereed: Yes
Series Name: Lecture Notes in Computer Science
ISBN: 9783540440253
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 06 Jul 2015 16:02
Last Modified: 05 Apr 2016 18:18

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