Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing
| dc.contributor.author | Chen, Hsinchun | |
| dc.contributor.author | Martinez, Joanne | |
| dc.contributor.author | Kirchhoff, Amy | |
| dc.contributor.author | Ng, Tobun Dorbin | |
| dc.contributor.author | Schatz, Bruce R. | |
| dc.date.accessioned | 2004-09-20T00:00:01Z | |
| dc.date.available | 2010-06-18T23:43:16Z | |
| dc.date.issued | 1998 | en_US |
| dc.date.submitted | 2004-09-20 | en_US |
| dc.identifier.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 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/106252 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley Periodicals, Inc | en_US |
| dc.subject | Information Extraction | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.subject.other | Information retrieval | en_US |
| dc.title | Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Journal of the American Society for Information Science, Special Issue on Management of Imprecision and Uncertainty in Information Retrieval and Database Management Systems | en_US |
| refterms.dateFOA | 2018-07-16T01:20:26Z | |
| html.description.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. |
