Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti · Proceedings · 2006
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.