@inproceedings{cdb1147e63534f808a38c027e22cb7fc,
title = "On-line new event detection using time window strategy",
abstract = "New Event Detection is the task of automatically detecting novel events from a temporally-ordered stream of news story documents. Traditionally, on-line new event detection system determines whether the incoming document contains a new event based on the history of all processed documents. With the improvement of processed documents, the efficiency of on-line new event detection will decrease. In this paper, we apply a time window strategy to new event detection. A group of new incoming documents in the time window are firstly clustered to obtain candidate topics. Next, these candidate topics are compared with the previously identified topics to determine whether a new topic is detected. The first story of the new detected topic, in temporal order, is regarded as a new event. By analyzing the news story documents within the time window in group, the new event detection efficiency is improved. Furthermore, the evaluations show that the time window processing strategy is helpful to improve the accuracy of new event detection.",
keywords = "Clustering, New Event Detection, Time Window",
author = "Xu, \{Rui Feng\} and Peng, \{Wei Hua\} and Jun Xu and Xiao Long",
year = "2011",
doi = "10.1109/ICMLC.2011.6016957",
language = "英语",
isbn = "9781457703065",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "1932--1937",
booktitle = "Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011",
address = "美国",
note = "10th International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",
}