RANDOM WALK TERM WEIGHTING FOR IMPROVED TEXT CLASSIFICATION
2007 ◽
Vol 01
(04)
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pp. 421-439
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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.
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2010 ◽
Vol 121-122
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pp. 996-1001
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2010 ◽
Vol 33
(8)
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pp. 1418-1426
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