A Competition-Based Connectionist Model for Information Retrieval Using a Merged Thesaurus.
Inien Syu, Sheau-Dong Lang:
A Competition-Based Connectionist Model for Information Retrieval Using a Merged Thesaurus.
CIKM 1994: 164-170@inproceedings{DBLP:conf/cikm/SyuL94,
author = {Inien Syu and
Sheau-Dong Lang},
title = {A Competition-Based Connectionist Model for Information Retrieval
Using a Merged Thesaurus},
booktitle = {Proceedings of the Third International Conference on Information
and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November
29 - December 2, 1994},
publisher = {ACM},
year = {1994},
pages = {164-170},
ee = {db/conf/cikm/SyuL94.html, http://doi.acm.org/10.1145/191246.191273},
crossref = {DBLP:conf/cikm/94},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
This paper investigates a network-based information retrieval model using diagnostic inferencing
techniques. A basic inference network in information retrieval consists of two component networks:
the document component and the query component. In our approach, there is a layer of nodes
corresponding to the documents, and a layer of nodes corresponding to the index terms extracted
from the document set, with links connecting documents to the related index terms 1. A thesaurus is
used to provide concept categories; these categories are represented by another layer of nodes, with
links connecting the index terms and the related categories 2. The query component uses a
symmetric structure. Each query causes markings of category nodes, hence markings of the related
index term nodes, in the document component of the network. In our previous work, we adapted a
competition-based connectionist model for diagnostic problem solving to information retrieval. In this
model, documents are treated as "disorders" and user information needs, represented by the marked
index term nodes, as "manifestations". A competitive activation mechanism is then used which
converges to a set of disorders that best explain the given manifestations. Our experiments showed
that the retrieval performance of this model is comparable to or better than that of various
information retrieval models reported in the literature. In this paper, we report further enhancements
of the model by using a merged thesaurus.
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BibTeX
Printed Edition
Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), Gaithersburg, Maryland, November 29 - December 2, 1994.
ACM 1994
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