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Anticipatory Event Detection for Bursty Events

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Intelligence and Security Informatics (PAISI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4430))

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Abstract

Anticipatory Event Detection (AED) is a framework for monitoring and tracking important and relevant news events at a fine grain resolution. AED has been previously tested successfully on news topics like NBA basketball match scores and mergers and acquisitions, but were limited to a static event representation model. In this paper, we discuss two recent attempts of adding content burstiness to AED. A burst is intuitively a sudden surge in frequency of some quantifiable measure, in our case, the document frequency. We examine two schemes for utilizing the burstiness of individual words, one for revamping the static document representation, and the other for extracting bursty and discriminatory words from the two states of the AED Event Transition Graph.

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References

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

    Kuiyu Chang, Qi He, Ridzwan Aminuddin, Ridzwan Suri & Ee-Peng Lim

Authors
  1. Kuiyu Chang
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  2. Qi He
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  3. Ridzwan Aminuddin
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  4. Ridzwan Suri
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  5. Ee-Peng Lim
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Editor information

Christopher C. Yang Daniel Zeng Michael Chau Kuiyu Chang Qing Yang Xueqi Cheng Jue Wang Fei-Yue Wang Hsinchun Chen

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© 2007 Springer Berlin Heidelberg

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Chang, K., He, Q., Aminuddin, R., Suri, R., Lim, EP. (2007). Anticipatory Event Detection for Bursty Events. In: Yang, C.C., et al. Intelligence and Security Informatics. PAISI 2007. Lecture Notes in Computer Science, vol 4430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71549-8_19

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  • DOI: https://doi.org/10.1007/978-3-540-71549-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71548-1

  • Online ISBN: 978-3-540-71549-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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