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

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	Similar Articles Articles by Stephan Bloehdorn Articles by Roberto Basili Articles by Marco Cammisa Articles by Alessandro Moschitti

Sixth IEEE International Conference on Data Mining (ICDM'06)

Hong Kong

December 18-December 22

ISBN: 0-7695-2701-9

	ASCII Text	x
	Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti, "Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity," Data Mining, IEEE International Conference on, pp. 808-812, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006.

	BibTex	x
	@article{ 10.1109/ICDM.2006.141, author = {Stephan Bloehdorn and Roberto Basili and Marco Cammisa and Alessandro Moschitti}, title = {Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2006}, issn = {1550-4786}, pages = {808-812}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.141}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity SN - 1550-4786 SP808 EP812 A1 - Stephan Bloehdorn, A1 - Roberto Basili, A1 - Marco Cammisa, A1 - Alessandro Moschitti, PY - 2006 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

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.

Citation:

Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti, "Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity," icdm, pp.808-812, Sixth IEEE International Conference on Data Mining (ICDM'06), 2006

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