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Random-Walk Term Weighting for Improved Text Classification

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3 Author(s)
Hassan, S. ; Univ. of North Texas, Denton ; Mihalcea, R. ; Banea, C.

This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier. The method uses term co-occurrence as a measure of dependency between word features. A random-walk model is applied on a graph encoding words and co-occurrence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. Experiments performed on three standard classification datasets show that the new random-walk based approach outperforms the traditional term frequency approach of feature weighting.

Published in:

Semantic Computing, 2007. ICSC 2007. International Conference on

Date of Conference:

17-19 Sept. 2007

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