Share Email Print

Proceedings Paper

Building a term association model for documents of interest
Author(s): Ishwar K. Sethi
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top