1,202 research outputs found
Relation Extraction Using Convolution Tree Kernel Expanded with Entity Features
PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200
Structured lexical similarity via convolution Kernels on dependency trees
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.
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