Fast and Effective Kernels for Relational Learning from Texts
Alessandro Moschitti - University of Trento, Italy
Fabio Massimo Zanzotto - University of Rome, Italy
In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering.