25 February 1999 Building a term association model for documents of interest
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ishwar K. Sethi, "Building a term association model for documents of interest", Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339972; https://doi.org/10.1117/12.339972


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