Paper: Structured Lexical Similarity via Convolution Kernels on Dependency Trees

ACL ID D11-1096
Title Structured Lexical Similarity via Convolution Kernels on Dependency Trees
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

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.