基于汉语框架网的中文问题分类

Chinese question classification based on Chinese FrameNet

问题分类是问答系统中重要的组成部分,问题分类结果的准确性直接影响到问答系统的质量。基于汉语框架网(Chinese FrameNet,CFN)提出了一种用于中文问题分类的新方法。该方法通过构建一系列汉语框架语义特征来表达每个问句的语义信息,进而使用最大熵模型进行中文问题的自动分类,与传统的问题分类技术相比,汉语框架语义信息的加入使得中文问题分类的精度得到了显著提高。实验结果进一步验证了该方法的有效性,大类和小类的分类精度分别达到了91.38%和83.20%。

Question classification is very important for question answering,and its accuracy affects the performance of the question answering system.This paper introduces a method of Chinese question classification based on Chinese FrameNet（CFN）.In this method,a series of features are firstly constructed to express each question＇s semantic information,and then Maximum Entropy Model is used to implement question classifier.Compared to the traditional methods,semantic information of Chinese FrameNet can improve the performance of question classification significantly.The experiment result shows that this method is effective and the classification accuracy of coarse classes and fine classes achieves 91.38% and 83.20% respectively.