Using star clusters for filtering
Proceedings of the ninth international conference on Information and knowledge management
Text classification and named entities for new event detection
Proceedings of the 27th 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
Using names and topics for new event detection
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
New event detection based on indexing-tree and named entity
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Global term weights in distributed environments
Information Processing and Management: an International Journal
Dynamic data assigning assessment clustering of streaming data
Applied Soft Computing
Online New Event Detection Based on IPLSA
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Topic detection and tracking with spatio-temporal evidence
ECIR'03 Proceedings of the 25th European conference on IR research
Giving temporal order to news corpus
CIS'04 Proceedings of the First international conference on Computational and Information Science
On-line single-pass clustering based on diffusion maps
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
A on-line news documents clustering method
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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This paper discusses the implementation and evaluation of a new-event detection system. We focus on a strict on-line setting, in that the system must indicate whether the current document contains or does not contain discussion of a new event before looking at the next document. Our approach to the problem uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. A corpus containing newswire and transcribed broadcast news was analyzed using our system, and our results compared favorably to those of other systems. We develop an evaluation methodology based on a combination of techniques that allows us to infer the expected performance of our approach in the field, and to suggest avenues for future research that may lead to better performance.