A hybrid convolution tree kernel is proposed in this paper to effectively model syntactic structures for semantic role labeling (SRL). The hybrid kernel consists of two individual convolution kernels: a Path kernel, which captures predicateargument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the novel hybrid convolution tree kernel outperforms the previous tree kernels. We also combine our new hybrid tree kernel based method with the standard rich flat feature based method. The experimental results show that the combinational method can get better performance than each of them individually.
REF presented a hybrid tree kernel which combines a constituent and a path kernel.
A Hybrid Convolution Tree Kernel For Semantic Role Labeling
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics - Poster Sessions
Computer Science