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    A hybrid approach to fuzzy name search incorporating language-based and textbased principles

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    Author
    Wu, Paul Horng Jyh
    Na, Jin Cheon
    Khoo, Christopher S.G.
    Issue Date
    2007
    Submitted date
    2006-07-10
    Keywords
    Information Retrieval
    Natural Language Processing
    Local subject classification
    fuzzy name search
    natural language processing
    information retrieval
    hybrid system
    language and text
    
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    Citation
    A hybrid approach to fuzzy name search incorporating language-based and textbased principles 2007, 33(1) Journal of Information Science
    Publisher
    SAGE Publications
    Journal
    Journal of Information Science
    URI
    http://hdl.handle.net/10150/105835
    Abstract
    Name Search is an important search function in various types of information retrieval systems, such as online library catalogs and electronic yellow pages. It is also difficult due to the high degree of fuzziness required in matching name variants. Previous approaches to name search systems use ad hoc combinations of search heuristics. This paper first discusses two approaches to name modelingâ the natural language processing (NLP) and the information retrieval (IR) modelsâ and proposes a hybrid approach. The approach demonstrates a critical combination of complementary NLP and IR features that produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transitionlikelihood, are introduced as the principles for integrating the advantageous aspects of both approaches. They have been implemented in an NLP- and IR- hybrid model system called Friendly Name Search (FNS) for real world applications in multilingual directory searches on the Singapore Yellow pages website.
    Type
    Journal Article (On-line/Unpaginated)
    Language
    en
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