Published: 2007 November
Herausgeber: Mario J. Silva and Alberto H. F. Laender and Ricardo A. Baeza-Yates and Deborah L. McGuinness and Bjoern Olstad and Oystein Haug Olsen and Andre O. Falcao
Buchtitel: Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM 2007), November 6-9, 2007, Lisbon, Portugal
Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.