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.
This research was partly supported by NSC under grant NSC 91-2213-E-001-019.
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Chen, C.C., Chen, YT., Sun, Y., Chen, M.C. (2003). Life Cycle Modeling of News Events Using Aging Theory. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds) Machine Learning: ECML 2003. ECML 2003. Lecture Notes in Computer Science(), vol 2837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39857-8_7
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DOI: https://doi.org/10.1007/978-3-540-39857-8_7
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