Measuring semantic similarity between words using lexical knowledge and neural networks

Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Bandar, Zuhair and Mclean, David 2002. Measuring semantic similarity between words using lexical knowledge and neural networks. Presented at: 3rd International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2002, Manchester, England, UK, 12-14 August 2002. Intelligent Data Engineering and Automated Learning — IDEAL 2002. Lecture Notes in Computer Science , vol.2412 Berlin and Heidelberg: Springer, pp. 111-116. 10.1007/3-540-45675-9_19

Full text not available from this repository.

Abstract

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:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	9783540440253
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129133

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item