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Home > Archive>Volume 21, Issue zk, 2010 >363-372
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On-Line Event Detection from Web News Stream
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    Abstract:

    In order to improve the efficiency of event detection from on-line news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements extracted from news (such as date, place, person and so on) to help detection task, and accomplish the detection efficiently with news characteristics matching, which decreases text similarity computation greatly. Experimental results show that our method improves on-line event detection performance, without sacrificing detection precision.

    Reference
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    [2] Yang Y, Pierce T, Carbonell J. A study on retrospective and on-line event detection. In: Proc. of the 21st ACM Int’l Conf. on Research and Development in Information Retrieval. 1998. 28–36.
    [3] Yang Y, Carbonell J, Brown R, Pierce T, Archibald BT, Liu X. Learning approaches for detecting and tracking news events. IEEE Intelligent Systems Special Issue on Applications of Intelligent Information Retrieval, 1999,14(4):32–43.
    [4] Allan J, Lavrenko V, Jin H. First story detection in TDT is hard. In: Proc. of the 9th Int’l Conf. on Information and Knowledge Management. ACM, 2000. 374-381.
    [5] Luo G, Tang C, Yu PS. Resource-Adaptive real-time new event detection. In: Chan CY, Ooi BC, Zhou A, eds. Proc. of the 2007 ACM SIGMOD Int’l Conf. on Management of data. ACM, 2007. 497–508.
    [6] Zamir O, Etzioni O. Web document clustering: A feasibility demonstration. In: Proc. of the 21st ACM Int’l Conf. on Research and Development in Information Retrieval. Melbourne: ACM Press, 1998. 46–54.
    [7] Brants T, Chen F. A system for new event detection. In: Proc. of the 26th ACM Int’l Conf. on Research and Development in Informaion Retrieval. ACM Press, 2003. 330–337.
    [8] Yang Y, Zhang J, Carbonell JG, Jin C. Topic conditioned novelty detection. In: Proc. of the 8th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. ACM, 2002. 688–693.
    [9] Kumaran G and Allan J. Text classification and named entities for new event detection. In: Proc. of the 27th ACM SIGIR International Conference on Research and Development in Information Retrieval. Sheffield: ACM Press, 2004. 297–304.
    [10] Zhang K, Zi J, Wu LG. New event detection based on indexing-tree and named entity. In: Proc. of the 30th ACM Int’l Conf. on Research and Development in Information Retrieval. ACM Press, 2007. 215–222.
    [11] Chen CC, Chen MC, Chen MS. An adaptive threshold framework for event detection using HMM-based life profiles. ACM Trans. on Information System, 2009,27(2).
    [12] Wang C, Zhang M, Ma S, Ru L. Automatic online news issue construction in Web environment. In: Huai J, Chen R, Hon HW, Liu Y, Ma WY, Tomkins A, Zhang X, eds. Proc. of the 17th Int’l Conf. on World Wide Web. Beijing: ACM, 2008. 457–466.
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Get Citation

付艳,周明全,王学松,栾华.面向互联网新闻的在线事件检测.软件学报,2010,21(zk):363-372

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History
  • Received:July 01,2010
  • Revised:December 10,2010
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