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    Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing

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    Author
    Chen, Hsinchun
    Martinez, Joanne
    Kirchhoff, Amy
    Ng, Tobun Dorbin
    Schatz, Bruce R.
    Issue Date
    1998
    Submitted date
    2004-09-20
    Keywords
    Information Extraction
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    Information retrieval
    
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    Show full item record
    Citation
    Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing 1998, 49(3):206-216 Journal of the American Society for Information Science, Special Issue on Management of Imprecision and Uncertainty in Information Retreival and Database Management Systems
    Publisher
    Wiley Periodicals, Inc
    Journal
    Journal of the American Society for Information Science, Special Issue on Management of Imprecision and Uncertainty in Information Retrieval and Database Management Systems
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/106252
    Abstract
    In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurrence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400,000/ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compared with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in concept recall, but in concept precision the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase â â varietyâ â in search terms and thereby reduce search uncertainty.
    Type
    Journal Article (Paginated)
    Language
    en
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