Measuring semantic similarity between words using lexical knowledge and neural networks

Yuhua Li, Zuhair Bandar, David Mclean

    Research output: Book/Report › Book

    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.
    LanguageEnglish
    Number of pages6
    Volume2412
    Publication statusPublished - 2002

    Publication series

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

    Fingerprint

    Semantics
    Neural networks

    Cite this

    Li, Y., Bandar, Z., & Mclean, D. (2002). Measuring semantic similarity between words using lexical knowledge and neural networks. (LECTURE NOTES IN COMPUTER SCIENCE).
    Li, Yuhua ; Bandar, Zuhair ; Mclean, David. / Measuring semantic similarity between words using lexical knowledge and neural networks. 2002. 6 p. (LECTURE NOTES IN COMPUTER SCIENCE).
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    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.",
    author = "Yuhua Li and Zuhair Bandar and David Mclean",
    note = "3rd International Conference on Intelligent Data Engineering and Automated Learning, MANCHESTER, ENGLAND, AUG 12-14, 2002",
    year = "2002",
    language = "English",
    isbn = "3-540-44025-9",
    volume = "2412",
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    publisher = "SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY",

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    Li, Y, Bandar, Z & Mclean, D 2002, Measuring semantic similarity between words using lexical knowledge and neural networks. LECTURE NOTES IN COMPUTER SCIENCE, vol. 2412.

    Measuring semantic similarity between words using lexical knowledge and neural networks. / Li, Yuhua; Bandar, Zuhair; Mclean, David.

    2002. 6 p. (LECTURE NOTES IN COMPUTER SCIENCE).

    Research output: Book/Report › Book

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    AU - Bandar, Zuhair

    AU - Mclean, David

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    PY - 2002

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    AB - 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.

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    SN - 3-540-44025-9

    VL - 2412

    T3 - LECTURE NOTES IN COMPUTER SCIENCE

    BT - Measuring semantic similarity between words using lexical knowledge and neural networks

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    Li Y, Bandar Z, Mclean D. Measuring semantic similarity between words using lexical knowledge and neural networks. 2002. 6 p. (LECTURE NOTES IN COMPUTER SCIENCE).