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
Extracting Temporal References to Assign Document Event-Time Periods
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
Structuralization of universes
Fuzzy Sets and Systems
Building a hierarchy of events and topics for newspaper digital libraries
ECIR'03 Proceedings of the 25th European conference on IR research
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In this paper we propose an incremental clustering algorithm for event detection, which makes use of the temporal references in the text of newspaper articles. This algorithm is hierarchically applied to a set of articles in order to discover the structure of topics and events that they describe. In the first level, documents 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 more complex events and topics can be discovered. The evaluation results demonstrate that regarding the temporal references of documents improves the quality of the system-generated clusters, and that the overall performance of the proposed system compares favorably to other on-line detection systems of the literature.