Case Element Joint Extraction Based on Case Field Correlation and Dependency Graph Convolutional Network

The case element is a brief description of the case-related events. Extracting the case elements in the news text has great significance for downstream case field natural language processing tasks. In view of the case field relevance and intrinsic relevance of the case elements, this paper proposes a joint case element extraction method based on case domain correlation and graph convolutional network: modeling sentence contextual information by bi-directional long short-term memory networks, then using it to predict the case field correlation for guarantying the elements’ relevance of cases by joint learning; and modeling the dependency relationship of candidate elements by graph convolutional network to capture its intrinsic relevance. The experiments show that the method proposed in this paper improves accuracy rate by 6. 6% in extracting case elements.