Measuring semantic similarity between words using lexical knowledge and neural networks

Yuhua Li, Zuhair Bandar, David Mclean

Research output: Book/ReportBookpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
PublisherSpringer
Number of pages6
Volume2412
ISBN (Print)3-540-44025-9
Publication statusPublished (in print/issue) - 2002

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE
PublisherSPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY

Bibliographical note

3rd International Conference on Intelligent Data Engineering and Automated Learning, MANCHESTER, ENGLAND, AUG 12-14, 2002

Fingerprint

Dive into the research topics of 'Measuring semantic similarity between words using lexical knowledge and neural networks'. Together they form a unique fingerprint.

Cite this