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Learning Approaches for Detecting and Tracking News Events
July/August 1999 (vol. 14 no. 4)
pp. 32-43

This article studies the effective use of information-retrieval and machine-learning techniques in a new task, event detection and tracking. The objective is to automatically detect novel events from chronologically ordered streams of news stories and to track events of interest over time. The authors extended existing supervised-learning and unsupervised-clustering algorithms to allow document classification based on both the information content and temporal aspects of events.

The authors conducted a task-oriented evaluation using Reuters and CNN news stories. They found that agglomerative document clustering is highly effective for retrospective event detection and that single-pass clustering with time windowing is a better choice for online alerting of novel events. For event tracking, k-nearest neighbor classification and a decision-tree approach demonstrated robust learning behavior, under the difficult condition where the number of positive training examples is extremely small.

Citation:
Yiming Yang, Jaime G. Carbonell, Ralf D. Brown, Thomas Pierce, Brian T. Archibald, Xin Liu, "Learning Approaches for Detecting and Tracking News Events," IEEE Intelligent Systems, vol. 14, no. 4, pp. 32-43, July-Aug. 1999, doi:10.1109/5254.784083
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