Document-Level Event Extraction via Human-Like Reading Process

• URL: http://arxiv.org/abs/2202.03092v1
• Date: Mon, 7 Feb 2022 12:10:58 GMT
• Title: Document-Level Event Extraction via Human-Like Reading Process
• Authors: Shiyao Cui, Xin Cong, Bowen Yu, Tingwen Liu, Yucheng Wang, Jinqiao Shi
• Abstract summary: Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. We propose a method called HRE, where DEE is decomposed into two iterative stages, rough reading and elaborate reading. Experiment results show the superiority of HRE over prior competitive methods.
• Score: 14.530050659066383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of one event record could reside in different sentences in the document, while the second one reflects one document may simultaneously contain multiple such event records. Motivated by humans' reading cognitive to extract information of interests, in this paper, we propose a method called HRE (Human Reading inspired Extractor for Document Events), where DEE is decomposed into these two iterative stages, rough reading and elaborate reading. Specifically, the first stage browses the document to detect the occurrence of events, and the second stage serves to extract specific event arguments. For each concrete event role, elaborate reading hierarchically works from sentences to characters to locate arguments across sentences, thus the scattering-arguments problem is tackled. Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled. Experiment results show the superiority of HRE over prior competitive methods.

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