Random-Walk Term Weighting for Improved Text Classification

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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.

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8 p.

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Hassan, Samer; Mihalcea, Rada, 1974- & Banea, Carmen September 2007.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 225 times , with 11 in the last month . More information about this paper can be viewed below.

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Description

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.

Physical Description

8 p.

Notes

Abstract: 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 works and co-occurence 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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  • Institute of Electrical and Electronics Engineers (IEEE) International Conference on Semantic Computing (ICSC), 2007, Irvine, California, United States

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • September 2007

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  • Jan. 31, 2011, 2:01 p.m.

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  • April 28, 2014, 2:06 p.m.

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Hassan, Samer; Mihalcea, Rada, 1974- & Banea, Carmen. Random-Walk Term Weighting for Improved Text Classification, paper, September 2007; (digital.library.unt.edu/ark:/67531/metadc30994/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.