DocumentCode
1544648
Title
Learning approaches for detecting and tracking news events
Author
Yang, Yiming ; Carbonell, Jaime G. ; Brown, Ralf D. ; Pierce, Thomas ; Archibald, Brian T. ; Liu, Xin
Author_Institution
Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
14
Issue
4
Firstpage
32
Lastpage
43
Abstract
The authors extend existing supervised-learning and unsupervised-clustering algorithms to allow document classification based on the information content and temporal aspects of news events. They´ve adapted several IR and machine learning techniques for effective event detection and tracking. The article discusses our research using manually segmented documents
Keywords
classification; information retrieval; learning (artificial intelligence); learning systems; pattern clustering; document classification; information content; information retrieval techniques; machine learning techniques; manually segmented documents; news event detection; news event tracking; supervised learning algorithms; temporal aspects; unsupervised clustering algorithms; Cities and towns; Computer crashes; Event detection; Floods; Jupiter;
fLanguage
English
Journal_Title
Intelligent Systems and their Applications, IEEE
Publisher
ieee
ISSN
1094-7167
Type
jour
DOI
10.1109/5254.784083
Filename
784083
Link To Document