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Volume 32 Issue 5
September  2017
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Pei-Feng Li, Guo-Dong Zhou. Three-Layer Joint Modeling of Chinese Trigger Extraction with Constraints on Trigger and Argument Semantics[J]. Journal of Computer Science and Technology, 2017, 32(5): 1044-1056. DOI: 10.1007/s11390-017-1780-5
Citation: Pei-Feng Li, Guo-Dong Zhou. Three-Layer Joint Modeling of Chinese Trigger Extraction with Constraints on Trigger and Argument Semantics[J]. Journal of Computer Science and Technology, 2017, 32(5): 1044-1056. DOI: 10.1007/s11390-017-1780-5
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Three-Layer Joint Modeling of Chinese Trigger Extraction with Constraints on Trigger and Argument Semantics

  • School of Computer Science and Technology, Soochow University, Suzhou 215006, China

Funds: The work was supported by the National Natural Science Foundation of China under Grant Nos. 61331011 and 61472265, and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization of China.
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  • Author Bio:

    Pei-Feng Li received his Ph.D., M.S., and B.S. degrees, all in computer science from Soochow University, Suzhou, in 2006, 1997 and 1994, respectively. Currently, he is a professor at the School of Computer Science and Technology, Soochow University, Suzhou. His current research interests include Chinese information processing, machine learning and information extraction.

  • Received Date: July 12, 2016
  • Revised Date: January 09, 2017
  • Published Date: September 04, 2017
    Abstract
  • As a subtask of Information Extraction (IE), which aims to extract structured information from a text, event extraction is to recognize event trigger mentions of a predefined event type and their arguments. In general, event extraction can be divided into two subtasks:trigger extraction and argument extraction. Currently, the frequent existences of un-annotated trigger mentions and poor-context trigger mentions impose critical challenges in Chinese trigger extraction. This paper proposes a novel three-layer joint model to integrate three components in trigger extraction, i.e., trigger identification, event type determination and event subtype determination. In this way, different evidence on distinct pseudo samples can be well captured to eliminate the harmful effects of those un-annotated trigger mentions. In addition, this paper introduces various types of linguistically driven constraints on trigger and argument semantics into the joint model to recover those poor-context trigger mentions. The experimental results show that our joint model significantly outperforms the state-of-the-art Chinese trigger extraction and Chinese event extraction as a whole.
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