Acta Scientiarum Naturalium Universitatis Pekinensis

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On-Line Topic Detection Using Named Entity Recognition

FU Yan, YANG Dongqing, TANG Shiwei, WU Wei, WANG Tengjiao, GAO Jun   

  1. Key Laboratory of High Confidence Software Technologies, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871;
  • Received:2008-04-02 Online:2009-03-20 Published:2009-03-20



  1. 高可信软件技术教育部重点实验室,北京大学信息科学技术学院,北京100871;

Abstract: In order to make on-line topic detection more efficient, a new method is proposed based on named entity recognition. New method extracts news elements from stories. Based on news elements, query composition is used to detect story link. This process reduces complex computation of text similarities. Experimental result indicates that the proposed method performs on-link topic detection accurately and efficiently.

Key words: on-line topic detection, named entity, named entity recognition, incremental clustering, suffix tree clustering

摘要: 为提高在线主题的检测效率,作者提出了一种基于实体识别技术的在线主题检测方法,利用新闻报道中的命名实体快速判断新到达报道与历史主题的关系,从而减少对报道间文本相似度的计算。实验结果显示,本文提出的方法能够在不牺牲检测准确率的基础上,显著提高在线主题检测的效率。

关键词: 在线主题检测, 命名实体, 实体识别, 增量聚类, 后缀树聚类

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