Two Learning Schemes in Information Retrieval.
Clement T. Yu, Hirotaka Mizuno:
Two Learning Schemes in Information Retrieval.
SIGIR 1988: 201-218@inproceedings{DBLP:conf/sigir/YuM88,
author = {Clement T. Yu and
Hirotaka Mizuno},
editor = {Yves Chiaramella},
title = {Two Learning Schemes in Information Retrieval},
booktitle = {SIGIR'88, Proceedings of the 11th Annual International ACM SIGIR
Conference on Research and Development in Information Retrieval,
Grenoble, France, June 13-15, 1988},
publisher = {ACM},
year = {1988},
pages = {201-218},
ee = {db/conf/sigir/YuM88.html},
crossref = {DBLP:conf/sigir/88},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Two methods are given to improve weighting schemes by using relevance information of a set of
queries. The first method is to estimate parameter values of two independence models in information
retrieval - the binary independence model and the non-binary independence model. The
parameters estimated here are used to calculate optimal weights for terms in a different set of
queries. Performance of this estimation is compared to the inverse document frequency method, the
cosine measure, and the statistical similarity measure. The second method is to learn optimal
weights of the non-binary independence model adaptively by a learning formula. Experiments are
performed on three different document collections CISI, MEDLARS, and CRN4NUL for both
methods, and results are reported. Both methods show improvements compared to the existing
weighting schemes. Experimental results show that the second method gives slightly better
performance than the first one, and has simpler implementation.
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BibTeX
Printed Edition
Yves Chiaramella (Ed.):
SIGIR'88, Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Grenoble, France, June 13-15, 1988.
ACM 1988
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