Intelligent fusion of structural and citation-based evidence for text classification

Por:

Zhang, Baoping (College teacher)

Colaborador(es):

New York Association for Computing Mochinery 2005Descrição: p. 667-668Tipo de conteúdo:

text

Tipo da mídia:

unmidiated

Tipo de armazenamento:

volume

Assunto(s):

Annual International ACM SIGIR Conference on Research and Development Information Retrieval , 28 SIGIR 2005Resumo: This paper shows different measures of similarity derived from the citation information and the structural contente (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetica Programming (GP) techniques. Our experiments with de ACM Computing CLassification Scheme, using documents from de ACM Digial Library, indicate that GP can discover similarity functions superior to those based solely on a single tupe of evidence

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This paper shows different measures of similarity derived from the citation information and the structural contente (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetica Programming (GP) techniques. Our experiments with de ACM Computing CLassification Scheme, using documents from de ACM Digial Library, indicate that GP can discover similarity functions superior to those based solely on a single tupe of evidence

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