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Inferring Public and Private Topics for Similar Events

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Semantic Web and Web Science

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

Abstract

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.

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References

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, People’s Republic of China

    Xubo Wen, Xiaoli Ma, Huan Xia & Juanzi Li

Authors
  1. Xubo Wen
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  2. Xiaoli Ma
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  3. Huan Xia
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  4. Juanzi Li
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Corresponding author

Correspondence to Xubo Wen .

Editor information

Editors and Affiliations

  1. , Dept. of Computer Science and Technology, Tsinghua University, Room 10-206, East main building, Beijing, 100084, China, People's Republic

    Juanzi Li

  2. , School of Comp. Sci. & Eng., Southeast University, Dongda Road 2, Nanjing, 211189, Jiangsu, China, People's Republic

    Guilin Qi

  3. Peking University, Inst. of Computer Science & Tech., North Zhongguancun Street 128, Beijing, 100871, China, People's Republic

    Dongyan Zhao

  4. L3S Research Center, Leibniz University Hannover, Appelstr. 4, Hannover, 30167, Germany

    Wolfgang Nejdl

  5. Tsinghua Campus H202B, Shenzhen City, 518055, China, People's Republic

    Hai-Tao Zheng

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Wen, X., Ma, X., Xia, H., Li, J. (2013). Inferring Public and Private Topics for Similar Events. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, HT. (eds) Semantic Web and Web Science. Springer Proceedings in Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6880-6_13

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  • DOI: https://doi.org/10.1007/978-1-4614-6880-6_13

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6879-0

  • Online ISBN: 978-1-4614-6880-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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