A Survey of Event Relation Extraction

Human beings recognize and understand the real world in units of events. In recent years, events have been used as the basic unit to process unstructured text in the field of natural language processing, but there is often a connection between events and events. Therefore, recognizing the relationship between events and events in unstructured text has become an important task in the field of natural language processing and has attracted more and more researchers’ attention. This paper first introduces the evolution of the method of event temporal relation and causal relation in the extraction research, comparing the advantages and disadvantages and method performance; Then, the event relation extraction model based on deep learning can be divided into strong supervision method and weak supervision method, and the extraction methods of event relation are analyzed, compared and summarized respectively, among them, the method of strong supervision based on deep learning can be further divided into pipeline method and joint learning method, and the method of weak supervision based on deep learning can be divided into semi-supervised learning method, remote learning supervised method and unsupervised learning method. Finally, this paper summarizes the methods of event relation extraction and points out the future research direction.