Ensemble Learning for Question Classification

摘要：

In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from questions corpus and the method of word semantic similarity is performed to adjust the feature weights. The ensemble methods, Bagging and AdaBoost, are applied to construct an ensemble of decision trees to tackle the problem of question classification respectively. Experiments on the Chinese question system of tourism domain show that the ensemble methods could effectively improve the classification accuracy.

关键词： question classification word semantic similarity ensemble learning bagging boosting

作者: Lei Su Hongzhi Liao Zhengtao Yu Quan Zhao

作者单位: School of Software,Yunnan University,Kunming 650091,China School of Information Engineering and Automation,Kunming University of Science and Technology,Kunmin

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2316-2320

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）