To address the low utilization of semantic information of event labels and the event relation in traditional extraction methods,an event extraction method that combined graph attention network(GAT)with question-answer(QA)extraction para-digm was proposed.Event types and argument roles were treated as query statements and an event relation graph based on the associations between different event types was constructed.The event type representation was optimized using graph attention networks,and an attention mechanism was employed to capture rich semantic information between the text and labels,thereby extracting event trigger words and event argument roles.Experimental results on the DuEE dataset demonstrate the effectiveness of the proposed method,and Fl values for trigger word recognition and argument role recognition are improved by 4.49％and 10.02％,respectively,compared to that of the traditional BERT_QA_Arg model.
维普
万方数据