DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, YC | ko |
dc.contributor.author | Choi, Key-Sun | ko |
dc.date.accessioned | 2013-02-27T18:55:22Z | - |
dc.date.available | 2013-02-27T18:55:22Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1996-09 | - |
dc.identifier.citation | INFORMATION PROCESSING MANAGEMENT, v.32, no.5, pp.543 - 553 | - |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.uri | http://hdl.handle.net/10203/70227 | - |
dc.description.abstract | Automatic thesaurus construction is accomplished by extracting term relations mechanically. A popular method uses statistical analysis to discover the term relations. For low-frequency terms, however, the statistical information of the arms cannot be reliably used for deciding the relationship of terms. This problem is generally referred to as the data-sparseness problem. Unfortunately, many studies have shown that low-frequency terms are of most use in thesaurus construction. This paper characterizes the statistical behavior of terms by using an inference network. A formal approach for the data-sparseness problem, which is crucial in constructing a thesaurus, is developed. The validity of this approach is shown by experiments. Copyright (C) 1996 Elsevier Science Ltd | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | PROBABILISTIC INFERENCE | - |
dc.subject | BELIEF NETWORKS | - |
dc.title | Automatic thesaurus construction using Bayesian networks | - |
dc.type | Article | - |
dc.identifier.wosid | A1996VJ87000004 | - |
dc.identifier.scopusid | 2-s2.0-0030244429 | - |
dc.type.rims | ART | - |
dc.citation.volume | 32 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 543 | - |
dc.citation.endingpage | 553 | - |
dc.citation.publicationname | INFORMATION PROCESSING MANAGEMENT | - |
dc.contributor.localauthor | Choi, Key-Sun | - |
dc.contributor.nonIdAuthor | Park, YC | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | PROBABILISTIC INFERENCE | - |
dc.subject.keywordPlus | BELIEF NETWORKS | - |
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