By Topic

Research of network hotspot detection and tracking model based on the characteristics of events

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wang Hu ; School of Management, Wuhan University of Technology, China ; Xiong Jiashu

In this paper, high performance real-time detecting and tracking technology for news events are proposed. The difference to previous TDT articles in the detection and tracking is that this attempt to establish the historical hotspot events Corpus first, and analyze the characteristics of the corpus according to timeline. HMM-based named entity recognition model is also used to find out other event characteristics except those reference timeline. By combining the above two methods and SVM, this study proposes its models in detecting and tracking. Experiments presented in this paper shows the models having a high performance in recall and precision.

Published in:

2010 International Conference on Computer Application and System Modeling (ICCASM 2010)  (Volume:1 )

Date of Conference:

22-24 Oct. 2010