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Proceedings Paper

Building a term association model for documents of interest
Author(s): Ishwar K. Sethi
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Paper Abstract

This paper addresses the problem of building a model for text documents of interest. Specifically, it considers a scenario where a large collection of documents, for example, the result of a search on the Internet, using one of the popular search engines, is given. Each document is indexed by certain keywords or terms. It is assumed that the user has identified a subset of documents that fits the user's needs. The goal is to build a term association model for the documents of interest, so that it can be used either for refining the user search or exported to other search engines/agents for further search of documents of interest. The model built is in the form of a unate Boolean function of the terms or keywords used in the initial search of documents. The proposed document model building algorithm is based on a modified version of the pocket algorithm for perceptron learning and a mapping method for converting neurons into equivalent symbolic representations.

Paper Details

Date Published: 25 February 1999
PDF: 6 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339972
Show Author Affiliations
Ishwar K. Sethi, Wayne State Univ. (United States)


Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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