Discovering event evolution graphs from news corpora
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-line single-pass clustering based on diffusion maps
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Event Duration Detection on Microblogging
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Extracting news blog hot topics based on the W2T Methodology
World Wide Web
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An event can be described by a sequence of chronological documents from several information sources that together describe a story or happening. The goal of event detection and tracking is to automatically identify events and their associated documents during their life cycles. Conventional document clustering and classification techniques cannot effectively detect and track sequential events, as they ignore the temporal relationships among documents related to an event. The life cycle of an event is analogous to living beings. With abundant nourishment (i.e., related documents for the event), the life cycle is prolonged; conversely, an event or living fades away when nourishment is exhausted. Improper tracking algorithms often unnecessarily prolong or shorten the life cycle of detected events. In this paper, we propose an aging theory to model the life cycle of sequential events, which incorporates a traditional single-pass clustering algorithm to detect and track events. Our experiment results show that the proposed method achieves a better overall performance for both long-running and short-term events than previous approaches. Moreover, we find that the aging parameters of the aging schemes are profile dependent and that using proper profile-specific aging parameters improves the detection and tracking performance further