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Discovering event episodes from news corpora: a temporal-based approach
Conference proceeding

Discovering event episodes from news corpora: a temporal-based approach

Chih-Ping Wei, Yen-Hsien Lee, Yu-Sheng Chiang, Jyun-Da Chen and Christopher Yang
Proceedings of the 11th International Conference on electronic commerce, pp 72-80
12 Aug 2009
DOI: https://doi.org/10.1145/1593254.1593265

Abstract

document clustering event detection event episode discovery event evolution feature selection
When performing environmental scanning, organizations typically deal with a numerous of events and topics about their core business, relevant technique standards, competitors, and market, where each event or topic to monitor or track generally is associated with many news documents. To reduce information overload and information fatigues when monitoring or tracking such events, it is essential to develop an effective event episode discovery mechanism for organizing all news documents pertaining to an event of interest. In this study, we propose a new metric, referred to as TFxIDF Tempo and develop a temporal-based event episode discovery technique that uses the proposed TFxIDF Tempo metric as its feature selection method and document representation scheme. Using the traditional TFxIDF-based HAC technique as performance benchmarks, our empirical evaluation results suggest that the proposed temporal-based event episode discovery technique outperforms its benchmark in cluster recall and cluster precision.

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4 citations in Scopus

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