Extracting Events from Web Documents for Social Media Monitoring Using Structured SVM

Yoonjae CHOI  Pum-Mo RYU  Hyunki KIM  Changki LEE  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.6   pp.1410-1414
Publication Date: 2013/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.1410
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Natural Language Processing
relation extraction,  structured SVM,  natural language processing,  information extraction,  

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Event extraction is vital to social media monitoring and social event prediction. In this paper, we propose a method for social event extraction from web documents by identifying binary relations between named entities. There have been many studies on relation extraction, but their aims were mostly academic. For practical application, we try to identify 130 relation types that comprise 31 predefined event types, which address business and public issues. We use structured Support Vector Machine, the state of the art classifier to capture relations. We apply our method on news, blogs and tweets collected from the Internet and discuss the results.