Online Hot Topic Detection from Web News Based on Bursty Term Identification

摘要：

　　With the increment in the volume of information,its almost impossible for people to assimilate all the news in time.A method to automatically detect hot topics from web news is strongly desired.Existing solutions take different perspectives ranging from identifying frequencies of terms to termsdistribution or part-of-speech characteristics.However,most of them are either too simplistic or unfitting to the properties of hot topics.Therefore,this paper presents a hot topic detection approach based on bursty term identification.We propose a new bursty term identification approach which considers both frequency and topicality properties to detect the bursty terms and hot topics.A series of experiments have demonstrated that our proposed approach has good performance compared with baseline methods.

关键词： Bursty term Weighting scheme Hot topic detection

作者: Chao Wang Xue Zhao Ying Zhang Xiaojie Yuan

作者单位: Nankai University,Tianjin 300353,Peoples Republic of China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 393-397

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）