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dc.contributor.authorChen, Hsinchun
dc.contributor.authorMartinez, Joanne
dc.contributor.authorKirchhoff, Amy
dc.contributor.authorNg, Tobun Dorbin
dc.contributor.authorSchatz, Bruce R.
dc.date.accessioned2004-09-20T00:00:01Z
dc.date.available2010-06-18T23:43:16Z
dc.date.issued1998en_US
dc.date.submitted2004-09-20en_US
dc.identifier.citationAlleviating 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 Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/106252
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn 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.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleAlleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computingen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Science, Special Issue on Management of Imprecision and Uncertainty in Information Retrieval and Database Management Systemsen_US
refterms.dateFOA2018-07-16T01:20:26Z
html.description.abstractIn 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.


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