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

Author

Hu, Wang ; Xiong Jiashu

Author_Institution

Sch. of Manage., Wuhan Univ. of Technol., Wuhan, China

Volume

1

fYear

2010

Abstract

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.

Keywords

data mining; hidden Markov models; information filtering; support vector machines; text analysis; HMM-based named entity recognition model; SVM; data mining; event characteristics; event filtering; network hotspot detection; network hotspot tracking; news events; recall performance; Character recognition; Educational institutions; Support vector machine classification; HMM-based named entity recognition model; SVM; characteristics of the event; event index;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Application and System Modeling (ICCASM), 2010 International Conference on

Print_ISBN

978-1-4244-7235-2

Electronic_ISBN

978-1-4244-7237-6

Type

conf

DOI

10.1109/ICCASM.2010.5619323

Filename

5619323