Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
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
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Document clustering with committees
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Topic-conditioned novelty detection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Event threading within news topics
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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Most previous research focus on organizing news set into flat collections of stories. However, a topic in news is more than a mere collection of stories: it is characterized by a definite structure of inter-related events. Stories within a topic usually share some terms which are related to the topic other than a specific event, so stories of different events are usually very similar to each other within a topic. To deal with this problem, we propose a new event identification method based on the term committee. We first capture some tight term clusters as term committees of potential events, and then use them to reweight the key terms in a story. The experimental results on two Linguistic Data Consortium (LDC) datasets show that the proposed method for event identification outperforms previous methods significantly.