The Maximum Entropy Principle in Information Retrieval.

Paul B. Kantor, Jung Jin Lee: The Maximum Entropy Principle in Information Retrieval. SIGIR 1986: 269-274

@inproceedings{DBLP:conf/sigir/KantorL86,
  author    = {Paul B. Kantor and
               Jung Jin Lee},
  title     = {The Maximum Entropy Principle in Information Retrieval},
  booktitle = {SIGIR'86, Proceedings of the 9th Annual International ACM SIGIR
               Conference on Research and Development in Information Retrieval,
                Pisa, Italy, September 8-10, 1986},
  publisher = {ACM},
  year      = {1986},
  pages     = {269-274},
  ee        = {db/conf/sigir/KantorL86.html},
  crossref  = {DBLP:conf/sigir/86},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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Abstract

Applications, assumptions and properties of the maximum entropy principle are discussed. The maximum entropy principle integrates prior estimates of relevance with the observed distribution of term combinations. The result may be a reordering of the segments of a database, compared to a naive estimate. Numerical examples obtained by solution of the non-linear equations for the dual variables are presented and discussed.

Copyright © 1986 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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SIGIR'86, Proceedings of the 9th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, September 8-10, 1986. ACM 1986
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