Affiliations: Research Center for Human-Computer Intelligence, Shenyang Aerospace University, Shenyang, Liaoning, China
Correspondence:
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Corresponding author: Hua Wu, Research Center for Human-Computer Intelligence, Shenyang Aerospace University, Shenyang, Liaoning 110000, China. E-mail: wuhua5027@163.com.
Abstract: Event extraction, as one of the difficult tasks of information extraction, can quickly obtain valuable information from the massive information on the Internet. This paper proposes a joint event extraction model based on RoBERTa-wwm-ext and gating mechanism for document-level long text data, which not only uses the prior knowledge from event types and pre-trained language models, but also uses gated fusion module to aggregate information in the event argument extraction tasks to enhance entity representation and splices entity type embedding, thereby enhancing the correlation among events, arguments and argument roles in the text, and improving the recognition accuracy of the arguments of each event in the document. Finally, the effectiveness of the model is verified on the public dataset.
Keywords: Event extraction, gated fusion, pre-trained language model, prior knowledge
