Automatic Thesaurus Construction Using Bayesian Networks.

@inproceedings{DBLP:conf/cikm/ParkHC95,
  author    = {Young C. Park and
               Young S. Han and
               Key-Sun Choi},
  title     = {Automatic Thesaurus Construction Using Bayesian Networks},
  booktitle = {CIKM '95, Proceedings of the 1995 International Conference on
               Information and Knowledge Management, November 28 - December
               2, 1995, Baltimore, Maryland, USA},
  publisher = {ACM},
  year      = {1995},
  pages     = {212-217},
  ee        = {db/conf/cikm/ParkHC95.html, http://doi.acm.org/10.1145/221270.221571},
  crossref  = {DBLP:conf/cikm/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Automatic thesaurus construction is accomplished by extracting term relations mechanically. A popular method uses statistical anaJysis to discover the term relations. For low frequency terms, however, the statistical information of the terms cannot be reliably used for deciding the relationship of terms. This problem is generally referred as data sparseness problem. Unfortunately, many studies have been said that the low frequency terms are of most use in information retrieval.

In this paper, we formalize the statistical behaviors of terms by using 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 © 1995 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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