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HowNet Based Chinese Question Classification

机译:基于知网的中文问题分类

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摘要

Question classification is the first step that Question Answering System must dispose, the precision of question classification greatly affect the subsequent processes. In this paper, we present a new question classification method which uses HowNet as the semantic resource to extract features, and we use Maximum Entropy Model to implement the method. The results validate the effectiveness of this method: the classification precision of coarse classes and fine classes reaches 92.18% and 83.86% respectively.
机译:问题分类是问答系统必须配置的第一步,问题分类的准确性极大地影响后续过程。本文提出了一种新的问题分类方法,该方法以知网为语义资源提取特征,并采用最大熵模型进行实现。结果证明了该方法的有效性:粗分类和细分类的分类精度分别达到92.18%和83.86%。

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