Event-relationship extraction,utilizing events as semantic units,automates the detection and extraction of logical connections between events in natural language texts.Event-relationship extraction is a critical direction in natural language processing and understanding,with significant applications in aviation security,finance,medicine,and public opinion analysis.Event relations come in many forms,and current research focuses primarily on temporal relations,causal relations,co-referential relations,and parent-child relations.We introduce the task of event relation extraction using these four relations as classification criteria.Firstly,we introduce and summarize the Chinese and English datasets commonly used in the four types of relationship extraction tasks.Then,we describe the concepts and application scenarios of the four types of relationship extraction tasks,introduce in detail the different methods and their rep-resentative models in each type of relationship extraction task,compare and analyze the strengths and weaknesses of each type of method on this basis,and summarize the experimental results and associated experimental data of each model;Finally,the current research problems of event-relationship extraction are reviewed,and the main research directions and development trends in the future are outlooked,providing recommendations for further enhancing the event-relationship extraction methods.
natural language processingevent relationship extractiondeep learningmachine learningsemantic information
万方数据
维普
