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Disambiguate Chinese Personal Pronoun Based on Semantic Structure

机译:基于语义结构消除歧义的汉语个人代词

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It is a very difficult problem in natural language processing to resolve the ambiguity of personal pronouns anaphora in a sentence or paragraph by computer according to semantic expression. Firstly, this paper focuses on finding out the personal names based on the maximal entropy model. Secondly, it categorizes Semantic Chunks and Sentence Category (SCs) based on the HNC theory. Thirdly, we chose 40 paragraphs in 2004 Athens Olympics as training corpus to make up the personal pronouns disambiguating rules. Finally, we chose another 40 paragraphs to exam the rules and processing steps by simulating computer's processing manually. We got a very high precision. Therefore, based on parallel semantic structure of HNC, the approach is effective for the disambiguating of Chinese personal pronouns.
机译:根据语义表达,自然语言处理中的自然语言处理中的歧义是一种非常困难的问题,以解决句子或段落中的句子或段落中的歧义。首先,本文侧重于根据最大熵模型找出个人名称。其次,它根据HNC理论分类了语义块和句子类别(SCS)。第三,我们在2004年雅典奥运会中选择了40段作为培训语料库,以弥补歧义规则的个人代词。最后,我们选择了另外40段来通过手动模拟计算机的处理来考试规则和处理步骤。我们有一个非常高的精度。因此,基于HNC的并行语义结构,该方法对于歧义中国人称代词是有效的。

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