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| Document-level Event Extraction Based on Joint Labeling and Global Reasoning |
| ZHONG Weifeng1, YANG Hang1,2, CHEN Yubo2, LIU Kang2, ZHAO Jun2 |
1.College of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China;
2. State Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract Current research on automatic event extraction focuses on sentence-level corpus. However, due to the complexity and the diversity of event description in texts, a complete event is mentioned by multiple sentences in many cases. This paper first proposes an Attention-based Sequence Labeling model for joint extraction of entities and events. Compared with the pipeline of entity extraction plus event recognition, this joint labeling model improves the F-score by 1%. Then, we use Multi-Layer Perception to label the entities in the events and identify their roles. Finally, based on the labeling and identification results, this paper leverages integer linear programming for global reasoning, improving the F-score of document-level event extraction by 3% compared to the baseline.
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Received: 04 January 2019
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2019, Vol. 33 
