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Semantic Kernels for Text Classification based on Topological Measures of Feature Similarity


Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti



Published: 2006 Dezember

Buchtitel: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06), Hong Kong, 18-22 December 2006
Seiten: 808 - 812
Referierte Veröffentlichung
BibTeX

Kurzfassung
Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, Kernel-based learning algorithms, especially Support Vector Machines, have become a dominant paradigm in the text mining community. This is also due to their capability to achieve more accurate learning results by incorporating a-priori knowledge by replacing standard linear kernels of bag-of-words with the so called 'semantic' kernels. In this paper we propose extensions and alternatives to previously proposed approaches to the design of semantic kernels by incorporating a variety of well-known measures of semantic similarity between terms. The experimental evaluation versus the standard linear kernel indicates that our approach improves performance in a variety of domains while being consistently superior in cases where little training data is available.

ISSN: 1550-4786
Weitere Informationen unter: LinkLink

Projekt

SEKT



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet
Ontologiebasierte Wissensmanagementsysteme, Text Mining, Maschinelles Lernen