Syntax-aware Event Temporal Relation Extraction Using Constraint Graph

Extracting temporal relations among events is essential in natural language understanding tasks. When two event mentions are widely separated in one text, the contextual information between them often becomes complicated and the temporal clues are difficult to locate, making inferring their temporal relationship more challenging. In this paper, we propose a novel approach named Constraint Graph-based and Syntax-aware Event Temporal Relation Extraction CGSE to address this issue. Specifically, we build temporal constraint rules by event attributes from databases to obtain prior temporal knowledge. Then we construct constraint graphs based on temporal constraint rules and present a graph neural network to model the dependencies. To eliminate irrelevant information in complicated contexts, we employ the Shortest Dependency Paths SDP between events in syntactic dependency parse trees, while also incorporating more temporal clues into the SDP. After that, we utilize a graph transformer to learn the representation of the SDP. Finally, a constraint fusion module is used to integrate constraint information and syntactic information to improve performance further. Experiments on two benchmark datasets, MATRES and TB-DENSE, establish that our proposed method demonstrates remarkable superiority over the previously existing state-of-the-art approaches in temporal relation extraction.