Relevance Feedback With Too Much Data.

James Allan: Relevance Feedback With Too Much Data. SIGIR 1995: 337-343

@inproceedings{DBLP:conf/sigir/Allan95,
  author    = {James Allan},
  editor    = {Edward A. Fox and
               Peter Ingwersen and
               Raya Fidel},
  title     = {Relevance Feedback With Too Much Data},
  booktitle = {SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR
               Conference on Research and Development in Information Retrieval.
                Seattle, Washington, USA, July 9-13, 1995 (Special Issue of
               the SIGIR Forum)},
  publisher = {ACM Press},
  year      = {1995},
  isbn      = {0-89791-714-6},
  pages     = {337-343},
  ee        = {db/conf/sigir/Allan95.html},
  crossref  = {DBLP:conf/sigir/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

BibTeX

Abstract

Modern text collections often contain large documents that span several subject areas. Such documents are problematic for relevance feedback since inappropriate terms can easily be chosen. This study explores the highly effective approach of feeding back passages of large documents. A less-expensive method that discards long documents is also reviewed and found to be effective if there are enough relevant documents. A hybrid approach that feeds back short documents and passages of long documents may be the best compromise.

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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BibTeX

Printed Edition

Edward A. Fox, Peter Ingwersen, Raya Fidel (Eds.): SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA, July 9-13, 1995 (Special Issue of the SIGIR Forum). ACM Press 1995, ISBN 0-89791-714-6
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