摘要
问答系统应能用准确、简洁的答案回答用户用自然语言提出的问题。问题分类是问答系统所要处理的第一步,分类结果的正确率直接影响后续工作的进行。本文提出了一种使用知网作为语义资源选取分类特征,并使用最大熵模型进行分类的新方法。该方法以问题的疑问词、句法结构、疑问意向词、疑问意向词在知网中的首义原作为分类特征。实验结果表明,在知网中选取的首义原能很好的表达问题焦点词的语义信息,可作为问题分类的一个主要特征。该方法能显著地提高问题分类的精度,大类和小类的分类精度分别达到了92.18%和83.86%。
Question answering system can provides a precise and concise answer to a natural language query. Question classification is the first task of Question Answering System, and the precision of question classification has great effect on the subsequent processes. In this paper, we present a new method on feature extraction which uses HowNet as semantic resource, and use Maximum Entropy Model to realize it. We choose the interrogative words, syntax structure, question focus words and their first sememes as classification feature. The experiment result＇show that the first sememes in HowNet can express the main meaning of the question focus words, ,it can he as an important feature. This method can improve the precision of question classification： the classification precision of coarse classes and fine classes reaches 92.18% and 83.86% respectively.
出处
《中文信息学报》
CSCD
北大核心
2007年第1期90-95,共6页
Journal of Chinese Information Processing
基金
国家航空基金(05J54011)
辽宁省自然科学基金(20042004)
关键词
计算机应用
中文信息处理
问答系统
问题分类
知网
最大熵模型
分类特征
computer application
Chinese information processing
question answering system
question classification
HowNet
maximum entropy model
classification feature
作者简介
孙景广（1981-），男，硕士生，主要研究方向为自然语言处理。
