2006 AHybridConvolTreeKernelForSRL

(CheZL06) ⇒ W. Che, M. Zhang, T. Liu, S. Li. (2006). “A Hybrid Convolution Tree Kernel for Semantic Role Labeling.” In: Proceedings of COLING/ACL-2006.

Subject Headings: Semantic Role Labelling, Convolution Tree Kernel

Notes

Cited By

Quotes

Abstract

“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.

Convolution Tree Kernels for SRL

"Moschitti (2004) proposed to apply convolution tree kernels (Collins and Duffy, 2001) to SRL. He selected portions of syntactic parse trees, which include salient sub-structures of predicatearguments, to define convolution kernels for the task of predicate argument classification. This portions selection method of syntactic parse trees is named as predicate-arguments feature (PAF) kernel. Figure 2 illustrates the PAF kernel feature space of the predicate buy and the argument Arg1 in the circled sub-structure. The kind of convolution tree kernel is similar to Collins and Duffy (2001)’s tree kernel except the sub-structure selection strategy. Moschitti (2004) only selected the relative portion between a predicate and an argument."

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 AHybridConvolTreeKernelForSRL	Min Zhang W. Che T. Liu S. Li			A Hybrid Convolution Tree Kernel for Semantic Role Labeling			http://acl.ldc.upenn.edu/P/P06/P06-2010.pdf