Dec. 18, 2006 to Dec. 22, 2006
Stephan Bloehdorn , University of Karlsruhe, Germany
Roberto Basili , University of Rome 'Tor Vergata', Italy
Marco Cammisa , University of Rome 'Tor Vergata', Italy
Alessandro Moschitti , University of Rome 'Tor Vergata', Italy
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.141
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.
Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti, "Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity", ICDM, 2006, Sixth International Conference on Data Mining (ICDM'06), Sixth International Conference on Data Mining (ICDM'06) 2006, pp. 808-812, doi:10.1109/ICDM.2006.141