Toward text understanding: classification of text documents by word map

Ari J. E. Visa; Jarmo Toivanen; Barbro Back; Hannu Vanharanta

doi:10.1117/12.381745

6 April 2000 Toward text understanding: classification of text documents by word map

Ari J. E. Visa, Jarmo Toivanen, Barbro Back, Hannu Vanharanta

Proceedings Volume 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II; (2000) https://doi.org/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.

Citation Download Citation

Ari J. E. Visa, Jarmo Toivanen, Barbro Back, and 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); https://doi.org/10.1117/12.381745

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available