A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Propagating Fine-Grained Topic Labels in News Snippets
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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In this paper we propose an incremental hierarchical clustering algorithm for on-line event detection. This algorithm is applied to a set of newspaper articles in order to discover the structure of topics and events that they describe. In the first level, articles with a high temporal-semantic similarity are clustered together into events. In the next levels of the hierarchy, these events are successively clustered so that composite events and topics can be discovered. The results obtained for the F1-measure and the Detection Cost demonstrate the validity of our algorithm for on-line event detection tasks.