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Báo cáo khoa học: "A Hybrid Convolution Tree Kernel for Semantic Role Labeling"

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 CoNLL2005 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. . | A Hybrid Convolution Tree Kernel for Semantic Role Labeling Wanxiang Che Harbin Inst. of Tech. Harbin China 150001 car@ir.hit.edu.cn Min Zhang Inst. for Infocomm Research Singapore 119613 mzhang@i2r.a-star.edu.sg Ting Liu Sheng Li Harbin Inst. of Tech. Harbin China 150001 tliu ls @ir.hit.edu.cn 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. 1 Introduction In the last few years there has been increasing interest in Semantic Role Labeling SRL . It is currently a well defined task with a substantial body of work and comparative evaluation. Given a sentence the task consists of analyzing the propositions expressed by some target verbs and some constituents of the sentence. In particular for each target verb predicate all the constituents in the sentence which fill a semantic role argument of the verb have to be recognized. Figure 1 shows an example of a semantic role labeling annotation in PropBank Palmer et al. 2005 . The PropBank defines 6 main arguments Arg0 is the Agent Arg1 is Patient etc. ArgM-may indicate adjunct arguments such as Locative Temporal. Many researchers Gildea and Jurafsky 2002 Pradhan et al. 2005a use feature-based methods NP 7 PRP j She ArgO bought DT NN IN NN V J j Ị. the silk in China Arg1 ArgM-LOC Figure 1 Semantic role labeling in a phrase structure syntactic tree