 Alleviating search uncertainty through concept associations: Automatic indexing, co‐occurrence analysis, and parallel computingHsinchun Chen, Joanne Martinez, Amy Kirchhoff, Tobun D. Ng and Bruce R. Schatz Journal of the American Society for Information Science, 1998, vol. 49, issue 3, 206-216 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 “variety” in search terms and thereby reduce search uncertainty. © 1998 John Wiley & Sons, Inc. Date: 1998 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jamest:v:49:y:1998:i:3:p:206-216 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the American Society for Information Science from Association for Information Science & Technology |
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