Paper: A Hybrid Convolution Tree Kernel For Semantic Role Labeling

ACL ID P06-2010
Title A Hybrid Convolution Tree Kernel For Semantic Role Labeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

A hybrid convolution tree kernel is pro- posed in this paper to effectively model syntactic structures for semantic role la- beling (SRL). The hybrid kernel consists of two individual convolution kernels: a Path kernel, which captures predicate- argument link features, and a Constituent Structure kernel, which captures the syn- tactic structure features of arguments. Evaluation on the datasets of CoNLL- 2005 SRL shared task shows that the novel hybrid convolution tree kernel out- performs the previous tree kernels. We also combine our new hybrid tree ker- nel based method with the standard rich flat feature based method. The experi- mental results show that the combinational method can get better performance than each of them individually.