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@inproceedings{DBLP:conf/sigir/Mauldin91,
author = {Michael L. Mauldin},
editor = {Abraham Bookstein and
Yves Chiaramella and
Gerard Salton and
Vijay V. Raghavan},
title = {Performance in FERRET: A Conceptual Information Retrieval System},
booktitle = {Proceedings of the 14th Annual International ACM SIGIR Conference
on Research and Development in Information Retrieval. Chicago,
Illinois, USA, October 13-16, 1991 (Special Issue of the SIGIR
Forum)},
publisher = {ACM},
year = {1991},
isbn = {0-89791-448-1},
pages = {347-355},
ee = {db/conf/sigir/Mauldin91.html},
crossref = {DBLP:conf/sigir/91},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
FERRET is a full text, conceptual information retrieval system that uses a partial understanding of its texts to provide greater precision and recall performance than keyword search techniques. It uses a machine-readable dictionary to augment its lexical knowledge and a variant of genetic learning to extend its script database.
Comparison of FERRET's retrieval performance on a collection of 1065 astronomy texts using 22 sample user queries with a standard boolean keyword query system showed that precision increased from 35 to 48 percent, and recall more than doubled, from 19.4 to 52.4 percent.
This paper describes the FERRET system's architecture, parsing and matching abilities, and focuses on the use of the the Webster's Seventh dictionary to increase the system's lexical coverage.
Copyright © 1991 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.