KGNet: A Legal Knowledge Enhancement and GlobalPointer Triple Extraction Network

Extracting entity relations is vital in legal artificial intelligence. It automates the mining of triple data from vast legal texts. Current methods face challenges in inaccurately identifying legal named entity boundaries and extracting over-lapping relation triples from legal texts. We present KGNet, a model developed to address these issues effectively. Our approach introduces a Word Information Generator Based on BMES tagging combined with the Fusionformer module. This innovation enhances the incorporation of legal domain knowl-edge into text representations, improving the accuracy of entity recognition. Additionally, we utilize the GlobalPointer decoder, which redefines and decomposes relation triples, thus resolving the issue of overlapping entities. Performance evaluations on a specially constructed judicial document dataset show that KGNet achieves an F1 score of 66.7%, representing an average improvement of 15.3% over baseline models. These results confirm the effectiveness of KGNet in enhancing legal document processing.