融合预训练模型与注意力的事件抽取方法OA北大核心

Event extraction aims to extract structured event information from a large amount of unstructured texts,but existing research work has problems such as difficulty in extracting overlapping roles,lack of interaction between sub-tasks,and insufficient semantic feature expression ability.This paper proposes a Chinese event extraction model PACJEE(pre-trained language model and attention mechanism based Chinese joint event extraction)to address these problems.The model uses the pre-trained language model RoBERTa to extract text features,then classifies the event types of the text,in the trigger word recognition stage,fuses the extracted event type prior features with the text features,and uses self-attention mechanism to obtain the internal feature relevance,in the argument role classification stage,introduces convolu-tional neural network and attention mechanism to enhance the trigger word feature expression ability,and finally uses multi-layer pointer tagging to identify overlapping roles.The method is experimentally analyzed on the Chinese datasets ACE2005 and DuEE,and the results show that compared with the baseline methods,the F1 values of trigger word classifi-cation are increased by 1.6 and 0.5 percentage points respectively,and the F1 values of argument role classification are increased by 3.3 and 2.5 percentage points respectively,indicating that the model can significantly improve the event extraction effect,and to a certain extent,improve the recognition accuracy of overlapping role events.