Engineering of syntactic features for shallow semantic parsing
Alessandro Moschitti, Bonaventura Coppola, Daniele Pighin, Roberto Basili · 2005
Recent natural language learning research has shown that structural kernels can be effectively used to induce accurate models of linguistic phenomena. In this paper, we show that the above prop-erties hold on a novel task related to predi-cate argument classification. A tree kernel for selecting the subtrees which encodes argument structures is applied. Experi-ments with Support Vector Machines on large data sets (i.e. the PropBank collec-tion) show that such kernel improves the recognition of argument boundaries. 1