Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing.
David A. Hull:
Improving Text Retrieval for the Routing Problem using Latent Semantic Indexing.
SIGIR 1994: 282-291@inproceedings{DBLP:conf/sigir/Hull94,
author = {David A. Hull},
editor = {W. Bruce Croft and
C. J. van Rijsbergen},
title = {Improving Text Retrieval for the Routing Problem using Latent
Semantic Indexing},
booktitle = {Proceedings of the 17th Annual International ACM-SIGIR Conference
on Research and Development in Information Retrieval. Dublin,
Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum)},
publisher = {ACM/Springer},
year = {1994},
isbn = {3-540-19889-X},
pages = {282-291},
ee = {db/conf/sigir/Hull94.html},
crossref = {DBLP:conf/sigir/94},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Latent Semantic Indexing (LSI) is a novel approach to information retrieval that attempts to model the underlying structure of term associations by transforming the traditional representation of documents as vectors of weighted term frequencies to a new coordinate space where both documents and terms are represented as linear combinations of underlying semantic factors.
In previous research, LSI has produced a small improvement in retrieval performance.
In this paper, we apply LSI to the routing task, which operates under the assumption that a sample of relevant and non-relevant documents is available to use in constructing the query.
Once again, LSI slightly improves performance.
However, when LSI is used is conduction with statistical classification, there is a dramatic improvement in performance.
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BibTeX
Printed Edition
W. Bruce Croft, C. J. van Rijsbergen (Eds.):
Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum).
ACM/Springer 1994, ISBN 3-540-19889-X
Contents BibTeX
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Referenced by
- Kothuri Venkata Ravi Kanth, Divyakant Agrawal, Ambuj K. Singh:
Dimensionality Reduction for Similarity Searching in Dynamic Databases.
SIGMOD Conference 1998: 166-176
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