6 April 2000 Toward text understanding: classification of text documents by word map
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Proceedings Volume 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II; (2000); doi: 10.1117/12.381745
Event: AeroSense 2000, 2000, Orlando, FL, United States
Abstract
In many fields, for example in business, engineering, and law there is interest in the search and the classification of text documents in large databases. To information retrieval purposes there exist methods. They are mainly based on keywords. In cases where keywords are lacking the information retrieval is problematic. One approach is to use the whole text document as a search key. Neural networks offer an adaptive tool for this purpose. This paper suggests a new adaptive approach to the problem of clustering and search in large text document databases. The approach is a multilevel one based on word, sentence, and paragraph level maps. Here only the word map level is reported. The reported approach is based on smart encoding, on Self-Organizing Maps, and on document histograms. The results are very promising.
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Ari J. E. Visa, Jarmo Toivanen, Barbro Back, Hannu Vanharanta, "Toward text understanding: classification of text documents by word map", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381745; https://doi.org/10.1117/12.381745
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Computer programming

Neural networks

Databases

Associative arrays

Binary data

Brain mapping

Quantization

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