Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Hedge Trimmer: a parse-and-trim approach to headline generation
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Toward a Common Event Model for Multimedia Applications
IEEE MultiMedia
Labeling chinese predicates with semantic roles
Computational Linguistics
F--a model of events based on the foundational ontology dolce+DnS ultralight
Proceedings of the fifth international conference on Knowledge capture
The stages of event extraction
ARTE '06 Proceedings of the Workshop on Annotating and Reasoning about Time and Events
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
Extracting violent events from on-line news for ontology population
BIS'07 Proceedings of the 10th international conference on Business information systems
Chinese News Event 5W1H Elements Extraction Using Semantic Role Labeling
ISIP '10 Proceedings of the 2010 Third International Symposium on Information Processing
Using compositional semantics and discourse consistency to improve Chinese trigger identification
Information Processing and Management: an International Journal
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To relieve "News Information Overload", in this paper, we propose a novel approach of 5W1H (who, what, whom, when, where, how) event semantic elements extraction for Chinese news event knowledge base construction. The approach comprises a key event identification step, an event semantic elements extraction step and an event ontology population step. We first use a machine learning method to identify the key events from Chinese news stories. Then we extract event 5W1H elements by employing the combination of SRL, NER technique and rule-based method. At last we populate the extracted facts of news events to NOEM, an event ontology designed specifically for modeling semantic elements and relations of events. Our experiments on real online news data sets show the reasonability and feasibility of our approach.