Joint event extraction based on hierarchical event schemas from framenet

Wei Li, Dezhi Cheng, Lei He, Yuanzhuo Wang*, Xiaolong Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Event extraction is useful for many practical applications, such as news summarization and information retrieval. However, the popular automatic context extraction (ACE) event extraction program only defines very limited and coarse event schemas, which may not be suitable for practical applications. FrameNet is a linguistic corpus that defines complete semantic frames and frame-to-frame relations. As frames in FrameNet share highly similar structures with event schemas in ACE and many frames actually express events, we propose to redefine the event schemas based on FrameNet. Specifically, we extract frames expressing event information from FrameNet and leverage the frame-to-frame relations to build a hierarchy of event schemas that are more fine-grained and have much wider coverage than ACE. Based on the new event schemas, we propose a joint event extraction approach that leverages the hierarchical structure of event schemas and frame-to-frame relations in FrameNet. The extensive experiments have verified the advantages of our hierarchical event schemas and the effectiveness of our event extraction model. We further apply the results of our event extraction model on news summarization. The results show that the summarization approach based on our event extraction model achieves significant better performance than several state-of-the-art summarization approaches, which also demonstrates that the hierarchical event schemas and event extraction model are promising to be used in the practical applications.

Original languageEnglish
Article number8643786
Pages (from-to)25001-25015
Number of pages15
JournalIEEE Access
Early online date18 Feb 2019
Publication statusPublished - 7 Mar 2019

Bibliographical note

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  • Event extraction
  • event schema definition
  • information extraction
  • joint inference


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