- Yiming Yang, J. Carbonell, Ralf D. Brown, Thomas Pierce, B. T. Archibald, Xin Liu. 1999. Learning Approaches to Topic Detection and Tracking. Abstract: This paper studies the eeective 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 track events of interest over time. We extended existing supervised learning and unsupervised clustering algorithms to allow document classiication based on both information content and temporal aspects of events. A task-oriented evaluation was conducted using Reuters and CNN news stories. We found agglomerative document clustering highly eeective (82% in the F 1 measure) for retrospective event detection, and single-pass clustering with time windowing a better choice for on-line alerting of novel events. We also observed robust learning behavior for k-nearest neighbor (kNN) classiication and a decision-tree approach in event tracking, under the diicult condition when the number of positive training examples is extremely small.
