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
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
First story detection in TDT is hard
Proceedings of the ninth international conference on Information and knowledge management
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using Burstiness to Improve Clustering of Topics in News Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Achieving both high precision and high recall in near-duplicate detection
Proceedings of the 17th ACM conference on Information and knowledge management
EventSearch: a system for event discovery and retrieval on multi-type historical data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Mining retrospective events from text streams has been an important research topic. Classic text representation model (i.e., vector space model) cannot model temporal aspects of documents. To address it, we proposed a novel burst-based text representation model, denoted as BurstVSM. BurstVSM corresponds dimensions to bursty features instead of terms, which can capture semantic and temporal information. Meanwhile, it significantly reduces the number of non-zero entries in the representation. We test it via scalable event detection, and experiments in a 10-year news archive show that our methods are both effective and efficient.