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Improving Chinese Semantic Role Labeling using High-quality Surface and Deep Case Frames

机译:使用高质量的表面和深层框架改进中文语义角色标签

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This paper presents a method for improving semantic role labeling (SRL) using a large amount of automatically acquired knowledge. We acquire two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.
机译:本文提出了一种使用大量自动获取的知识来改进语义角色标签(SRL)的方法。我们获得了两种知识,分别称为表面案例框架和深层案例框架。尽管表面情况框架是从语法分析中编译的,并且可以用作丰富的语法知识,但它们解决语义歧义性的能力有限。为了弥补表面案例框架的不足,我们根据自动语义角色来编译深层案例框架。我们还考虑了两种知识的质量管理,以消除自动分析带来的噪音。实验结果表明,可以通过自动获取知识来改进中文SRL,并且质量管理对此任务具有积极作用。

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