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

Yuhua Li, Zuhair Bandar, David McLean

    Research output: Contribution to journalArticle

    828 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)871-882
    JournalIEEE Transactions on Knowledge and Data Engineering
    Volume15
    Issue number4
    Publication statusPublished - Jul 2003

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