Using cognitive model to automatically analyze Chinese predicate

Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu
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Abstract

This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.
基于认知模型的汉语谓词自动分析
本文提出了一种基于构建-集成(CI)模型扩展的汉语语义角色标注认知方法。与机器学习方法相比,该方法可以隐式地将更多的上下文和一般知识集成到计算过程中。首先,我们定义了一个命题表示作为语义角色标注的基本单元。然后通过模拟人脑的扩张性激活,强化情境适宜命题,抑制情境不适宜命题。最后,实验结果表明,在中文PropBank (CPB)和其他两个数据集上取得了令人鼓舞的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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