摘要
Event extraction(EE)is a significant part of natural language information extraction,and it is widely adopted in other natural language processing(NLP)tasks such as question answering and machine reading comprehension.With the development of the NLP field,numerous datasets and approaches for EE are promoted,raising the need for a comprehensive review.In this paper,the resources for EE are reviewed,and then the numerous neural network models currently employed in EE tasks are classified into three types:Word sequence-based methods,graph-based neural network methods,and external knowledge-based approaches.And then the methods are compared and contrasted in detail,and their flaws and difficulties are analyzed with existing research in this survey.Finally,the future research tendency is discussed for EE.
基金
supported by the National Natural Science Foundation of China(U21A20488)。
