Relevance Feedback and Inference Networks.
David Haines, W. Bruce Croft:
Relevance Feedback and Inference Networks.
SIGIR 1993: 2-11@inproceedings{DBLP:conf/sigir/HainesC93,
author = {David Haines and
W. Bruce Croft},
editor = {Robert Korfhage and
Edie M. Rasmussen and
Peter Willett 0002},
title = {Relevance Feedback and Inference Networks},
booktitle = {Proceedings of the 16th Annual International ACM-SIGIR Conference
on Research and Development in Information Retrieval. Pittsburgh,
PA, USA, June 27 - July 1, 1993},
publisher = {ACM},
year = {1993},
isbn = {0-89791-605-0},
pages = {2-11},
ee = {db/conf/sigir/HainesC93.html},
crossref = {DBLP:conf/sigir/93},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Relevance feedback, which modifies queries using judgements of the relevance of a few,
highly-ranked documents, has historically been an important method for increasing the performance
of information retrieval systems. In this paper, we extend the inference network model introduced by
Turtle and Croft to include relevance feedback techniques. The difference between relevance
feedback on text abstracts and full text collections is studied. Preliminary results for relevance
feedback on the structured queries supported by the inference net model are also reported.
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BibTeX
Printed Edition
Robert Korfhage, Edie M. Rasmussen, Peter Willett (Eds.):
Proceedings of the 16th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Pittsburgh, PA, USA, June 27 - July 1, 1993.
ACM 1993, ISBN 0-89791-605-0
Contents BibTeX
Citation page
Referenced by
- James R. Chen, Nathalie Mathe:
Learning Subjective Relevance to Facilitate Information Access.
CIKM 1995: 218-225
BibTeX
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