Structured lexical similarity via convolution Kernels on dependency trees

Abstract

A central topic in natural language process-ing is the design of lexical and syntactic fea-tures suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical simi-larities. We define efficient and powerful ker-nels for measuring the similarity between de-pendency structures, whose surface forms of the lexical nodes are in part or completely dif-ferent. The experiments with such kernels for question classification show an unprecedented results, e.g. 41 % of error reduction of the for-mer state-of-the-art. Additionally, semantic role classification confirms the benefit of se-mantic smoothing for dependency kernels.