A document-level event extraction method based on core arguments(CA-DocEE)is proposed,which defines criteria for selecting core arguments based on their distributions in document-level events,uses heterogeneous graph convolutional neural net-works to augment document contextual information for encoding argument entities,and captures deep semantic information in sen-tences based on machine reading comprehension methods for classifying the role of arguments.On the document-level event extrac-tion dataset,the method proposed in this paper achieves a micro-average F1 value of 80.1%,which is comparable with the state-of-the-art methods.
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