Weighted kernel model for text categorization

Authors:
Lei Zhang;Debbie Zhang;Simeon J. Simoff;John Debenham
Affiliations:
University of Technology, Sydney, Broadway NSW, Australia;University of Technology, Sydney, Broadway NSW, Australia;University of Technology, Sydney, Broadway NSW, Australia;University of Technology, Sydney, Broadway NSW, Australia
Venue:
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Year:
2006

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Abstract

Traditional bag-of-words model and recent word-sequence kernel are two well-known techniques in the field of text categorization. Bag-of-words representation neglects the word order, which could result in less computation accuracy for some types of documents. Word-sequence kernel takes into account word order, but does not include all information of the word frequency. A weighted kernel model that combines these two models was proposed by the authors [1]. This paper is focused on the optimization of the weighting parameters, which are functions of word frequency. Experiments have been conducted with Reuter's database and show that the new weighted kernel achieves better classification accuracy.