DocumentCode :
2382930
Title :
On-line event detection from web news stream
Author :
Fu, Yan ; Zhou, Ming-quan ; Wang, Xue-song ; Luan, Hua
Author_Institution :
Coll. of Inf. Sci. & Technol., Beijing Normal Univ., Beijing, China
fYear :
2010
fDate :
1-3 Dec. 2010
Firstpage :
105
Lastpage :
110
Abstract :
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental results show that our method improves on-line event detection performance, without sacrificing detection precision.
Keywords :
Internet; pattern clustering; publishing; text analysis; trees (mathematics); Web news stream; detection efficiency; detection precision; detection process; detection task; informative elements; named entity recognition; online event detection; suffix tree clustering; text similarity computation; window-adding; named entity recognition; on-line event detection; suffix-tree clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing and Applications (ICPCA), 2010 5th International Conference on
Conference_Location :
Maribor
Print_ISBN :
978-1-4244-9144-5
Type :
conf
DOI :
10.1109/ICPCA.2010.5704083
Filename :
5704083
Link To Document :
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