Combining structural and citation-based evidence for text classification

Authors:
Baoping Zhang;Marcos André Gonçalves;Weiguo Fan;Yuxin Chen;Edward A. Fox;Pável Calado;Marco Cristo
Affiliations:
Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil
Venue:
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Year:
2004

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Abstract

This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.