Advertisement

Life Cycle Modeling of News Events Using Aging Theory

  • Chien Chin Chen
  • Yao-Tsung Chen
  • Yeali Sun
  • Meng Chang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches.

Keywords

Time Slot Event Detection Aging Theory News Event Life Cycle Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
  2. 2.
  3. 3.
    Allan, J., Papka, R., Lavrenko, V.: On-Line New Event Detection and Tracking. In: proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp. 37-45 (1998)Google Scholar
  4. 4.
    Allan, J., Carbonell, J., Doddington, G., 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 (1998)Google Scholar
  5. 5.
    Billsus, D., Pazzani, M.J.: A Personal News Agent that Talks, Learns and Explains. In: Third International Conference on Autonomous Agents (1999)Google Scholar
  6. 6.
    Chen, C.C., Chen, M.C., Sun, Y.: PVA: A Self-Adaptive Personal View Agent. Journal of Intelligent Information Systems 18(2/3), 173–194 (2002)CrossRefGoogle Scholar
  7. 7.
    Hsu, W.-L., Lang, S.-D.: Classificaiton Algorithms for NETNEWS Articles. In: Proceedings of 8th ACM international conference on information and knowledge management, pp. 114-121 (1999)Google Scholar
  8. 8.
    Kleinberg, J.: Bursty and Hierarchical Structure in Streams. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 91-101 (2002)Google Scholar
  9. 9.
    Lin, S.-H., Chen, M.C., Ho, J.-M., Huang, Y.-M.: ACIRD: Intelligent Internet Documents Organization and Retrieval. IEEE Transactions on Knowledge and Data Engineering 14(3) (May/June 2002)Google Scholar
  10. 10.
    Menczer, F., Belew, R.K., Willuhn, W.: Artificial Life Applied to Adaptive Information Agents. In: Spring Symposium on Information Gathering from Distributed, Heterogeneous Database, AAAI Press, Menlo Park (1995)Google Scholar
  11. 11.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  12. 12.
    Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: The SMART Retrieval System, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  13. 13.
    Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading (1989)Google Scholar
  14. 14.
    Yang, Y., Pierce, T., Carbonell, J.: A Study on Retrospective and On-Line Event Detection. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp. 28–36 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chien Chin Chen
    • 1
  • Yao-Tsung Chen
    • 1
  • Yeali Sun
    • 2
  • Meng Chang Chen
    • 1
  1. 1.Institute of Information ScienceAcademia SinicaTaiwan
  2. 2.Dept. of Information ManagementNational Taiwan UniversityTaiwan

Personalised recommendations