Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel
Semantic Role Labeling Using a Grammar-Driven Convolution Tree Kernel
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DOI：
10.1109/tasl.2008.2001104
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发表时间：
2008-09
期刊：
影响因子：
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通讯作者：
Min Zhang;Wanxiang Che;Guodong Zhou;AiTi Aw;C. Tan;Ting Liu;Sheng Li
中科院分区：
文献类型：
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作者：
Min Zhang;Wanxiang Che;Guodong Zhou;AiTi Aw;C. Tan;Ting Liu;Sheng Li
卷积树核在语义角色标注（SRL）中表现出了良好的效果。然而，该内核在设计时没有考虑太多的语言知识，只进行子树之间的硬匹配。为了克服这些限制，本文提出了一个语法驱动的卷积树核SRL通过引入更多的语言知识。与标准卷积树核相比，该核具有两个优点：1）语法驱动的近似子结构匹配; 2）语法驱动的近似树节点匹配。这两个近似匹配机制使建议的内核，以更好地探索语言动机的结构化知识。在CoNLL-2005 SRL共享任务和PropBank I语料库上的实验表明，该核的性能明显优于标准卷积树核.此外，我们提出了一个复合内核集成基于特征的多项式内核和建议的语法驱动的卷积树内核的SRL。实验结果表明，我们的复合核为基础的方法显着优于以前最好的报告。
Convolution tree kernel has shown promising results in semantic role labeling (SRL). However, this kernel does not consider much linguistic knowledge in kernel design and only performs hard matching between subtrees. To overcome these constraints, this paper proposes a grammar-driven convolution tree kernel for SRL by introducing more linguistic knowledge. Compared with the standard convolution tree kernel, the proposed grammar-driven kernel has two advantages: 1) grammar-driven approximate substructure matching, and 2) grammar-driven approximate tree node matching. The two approximate matching mechanisms enable the proposed kernel to better explore linguistically motivated structured knowledge. Experiments on the CoNLL-2005 SRL shared task and the PropBank I corpus show that the proposed kernel outperforms the standard convolution tree kernel significantly. Moreover, we present a composite kernel to integrate a feature-based polynomial kernel and the proposed grammar-driven convolution tree kernel for SRL. Experimental results show that our composite kernel-based method significantly outperforms the previously best-reported ones.