Incorporating Latent Semantic Indexing into a Neural Network Model for Information Retrieval.

Inien Syu, Sheau-Dong Lang, Narsingh Deo: Incorporating Latent Semantic Indexing into a Neural Network Model for Information Retrieval. CIKM 1996: 145-153

@inproceedings{DBLP:conf/cikm/SyuLD96,
  author    = {Inien Syu and
               Sheau-Dong Lang and
               Narsingh Deo},
  title     = {Incorporating Latent Semantic Indexing into a Neural Network
               Model for Information Retrieval},
  booktitle = {CIKM '96, Proceedings of the Fifth International Conference on
               Information and Knowledge Management, November 12 - 16, 1996,
               Rockville, Maryland, USA},
  publisher = {ACM},
  year      = {1996},
  pages     = {145-153},
  ee        = {db/conf/cikm/SyuLD96.html, http://doi.acm.org/10.1145/238355.238475},
  crossref  = {DBLP:conf/cikm/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We incorporate the Latent Semantic Indexing (LSI) technique into a competition-based neural network model for information retrieval. The original neural network model was based on a causal inference network, incorporating Roget's Thesaurus, that connects the index terms and related documents. Since the process of creating or updating a thesaurus is rather expensive, we apply the LSI technique to provide an automated procedure that captures the semantic relationship between the documents and index terms. Our experimental results using four standard text collections show that the LSI-based model generates appreciable improvement in retrieval effectiveness with faster query evaluation over the thesaurus-based model.

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CIKM '96, Proceedings of the Fifth International Conference on Information and Knowledge Management, November 12 - 16, 1996, Rockville, Maryland, USA. ACM 1996
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