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- W2095916112 abstract "Daily, several news agencies publish thousands of articles concerning many events of all types (political, economic, cultural, etc.). The decision-makers find themselves in front of a great number of events, a few of which concern them. The automatic treatment of such events becomes increasingly necessary. Thus, we propose a machine learning-based approach that allows annotating news articles to generate an automatic summary of the events. We propose a new similarity measurement between events and we validate our approach by the development of the AnnotEv system." @default.
- W2095916112 created "2016-06-24" @default.
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- W2095916112 date "2009-01-01" @default.
- W2095916112 modified "2023-09-25" @default.
- W2095916112 title "Automatic Annotation Approach of Events in News Articles" @default.
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