Inferring Public and Private Topics for Similar Events

Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Event detection, extraction, and tracking can help people to better understand the event that happened in the world. Previous research focuses on mining single event. In this paper, we propose a topic model to infer the public and private topic from a group of similar events. Aiming at the consistency and mapping of topics, this model discriminates public and private topics by using Bernoulli distribution to determine the source of words. Experiment on earthquake dataset shows that our proposed algorithm can induce the public and private topics acceptable by users.



The work is supported by the Natural Science Foundation of China (No. 61035004, No. 60973102), 863 High Technology Program (2011AA01A207), European Union 7th framework project FP7-288342, and THU-NUS NExT Co-Lab and the project cooperated with Chongqing research institute of science and technology.


  1. 1.
    Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pp. 194–218. Lansdowne, VA (1998)Google Scholar
  2. 2.
    Blei, D.M., Lafferty, J.D.: Correlated topic models. In: Advances in Neural Information Processing Systems 18, Vancouver, BC (2005)Google Scholar
  3. 3.
    Li, W., McCallum, A.: Pachinko allocation: dag-structured mixture models of topic correlations. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 577–584. ACM, Pittsburgh (2006)Google Scholar
  4. 4.
    Hong, L., Dom, B., Gurumurthy, S., Tsioutsiouliklis, K.: A time-dependent topic model for multiple text streams. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 832–840. ACM, San Diego (2011)Google Scholar
  5. 5.
    Paul, M.: Cross-collection topic models: automatically comparing and contrasting text. Bachelor Degree. Advisor: Girju, R. Department of Computer Science. University of Illinois at Urbana-Champaign (2009)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingPeople’s Republic of China

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