Text Categorization Based on Emergency domain Words

摘要：

Most of the research on text categorization didnt consider the characteristics of the emergency domain. Considering the characters of a specific emergency domain, we propose a text classification based on emergency domain words and machine learning technique. With CHI as evaluation function to select text features, the addition of emergency domain words, Maximum Entropy classifier and KNN classifier, we conduct a series of experiments on emergency event texts classification. The experiments shows that, the introduction of emergency domain words will increase the average accuracy of maximum entropy classifier and KNN classifier by 4％ to 5％. Particularly maximum entropy classifier can still get an average accuracy rate as 97.0％ after the introduction of the emergency domain terms.

关键词： Text Classification Emergency domain Words Maximum Entropy

作者: Yan Zhao Yuguang Wang

作者单位: School ofManagement.Graduate university of Chinese Academy of Sciences,Beijingl 00190, China School of Computer Science and Technology. Tianjin University, Tianjini00l92, China

会议类型: 国际会议

会议名称: International Symposium on Emergency Management 2011(ISEM‘2011)(第六届国际应急管理论坛暨中国(双法)应急管理专业委员会第七届年会)

会议地点: 北京

会议语种:英文

页码: 10-16

在线出版日期: 2011-11-19（万方平台首次上网日期，不代表论文的发表时间）