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

Authors:
Stephan Bloehdorn;Roberto Basili;Marco Cammisa;Alessandro Moschitti
Affiliations:
University of Karlsruhe, Germany;University of Rome 'Tor Vergata', Italy;University of Rome 'Tor Vergata', Italy;University of Rome 'Tor Vergata', Italy
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Abstract

In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.