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基于实体识别的在线主题检测方法 被引量：4

On-Line Topic Detection Using Named Entity Recognition
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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。 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.
作者 付艳 杨冬青 唐世渭 伍伟 王腾蛟 高军
机构地区 高可信软件技术教育部重点实验室
出处 《北京大学学报（自然科学版）》 EI CAS CSCD 北大核心 2009年第2期227-232,共6页 Acta Scientiarum Naturalium Universitatis Pekinensis
基金 国家自然科学基金(60473051 60503037) 国家高技术研究发展计划专项经费(2006AA01Z230 2007AA01Z191)资助
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 on-line topic detection named entity named entity recognition incremental clustering suffix tree clustering
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

  • 1Allan J, Carbonell J, Doddington G, et al. Topic detection and tracking pilot study: Final report// Proceedings of the DARPA Broadcast News Transcription and Understanding
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  • 3Yang Y, Carbonell J, Brown R, et al. Learning approaches for detecting and tracking news events. IEEE Intelligent Systems: Special Issue on Applications of Intelligent Information Retrieval, 1999, 14(4):32-43
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二级参考文献30

  • 1刘群,张华平,俞鸿魁,程学旗.基于层叠隐马模型的汉语词法分析[J].计算机研究与发展,2004,41(8):1421-1429. 被引量：198
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共引文献221

同被引文献75

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  • 2俞鸿魁,张华平,刘群,吕学强,施水才.基于层叠隐马尔可夫模型的中文命名实体识别[J].通信学报,2006,27(2):87-94. 被引量：157
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引证文献4

二级引证文献12

北京大学学报（自然科学版）
2009年 第2期

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