Computer Science ›› 2020, Vol. 47 ›› Issue (12): 233-238.doi: 10.11896/jsjkx.191100031

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Chinese Event Detection Based on Document Information and Bi-GRU

ZHU Pei-pei, WANG Zhong-qing, LI Shou-shan, WANG Hong-ling   

  1. School of Computer Science and Technology Soochow University Suzhou Jiangsu 215006,China
  • Received:2019-11-05 Revised:2019-12-30 Published:2020-12-17
  • About author:ZHU Pei-pei,born in 1995postgra-duateis a member of China Computer Federation.Her main research interests include natu-ral language processing and so on.
    WANG Zhong-qing,born in 1987Ph.D.His main research interests include natural language processing and so on.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(61806137,61702518) and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China (18KJB520043).

Abstract: Event extraction is an important research task in information extraction and event detection is the key to event extraction.Existing Chinese neural network event detection methods are sentence-based and the local context information obtained by this method is not enough to resolve the event triggers semantic ambiguity.In order to solve this problemthis paper studies document information effects.Firstlybased on the bidirectional gated recurrent units network (Bi-GRU)this paper defines three windows to learn sentence features.Thenthe sentence-level representation is concatenated and the document features are learned by using the bidirectional gated recurrentunits network.Finallyto enrich the semantic information of sentences and reduce the event-trigger sematic event triggers ambiguityit merges the sentence-level representation and the document-level representation and then classifies eventtriggers through the Softmax function.Experimental results on the ACE2005 dataset show that the sentences-context representation can improve the Chinese event detection performance and this event detection method outperforms state-of-the-art results by 1.5% on F1.

Key words: ACE2005, Bidirectional gated recurrent units, Document information, Event detection, Event extraction

CLC Number: 

  • TP391
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