面向法律领域的实体和关系抽取OA北大核心

Entity and relation extraction technology in the Chinese judicial field plays an important role in improving case-handling efficiency.However,existing models lack domain knowledge and encounter challenges in handling overlapping entities,leading to difficulties in accurately distinguishing and extracting relationships.By introducing domain knowledge,we propose a legal information enhancement module that enhances the ability of the legal potential relationship and global correspondence(LPRGC)model to understand legal terms,rules,and contextual information,thereby improving the performance of entity and relation extraction algorithms.To address the issue of overlapping entities,we design a relationship extraction method based on latent relationships and entity alignment.By precisely annotating entity positions,filtering potential relationships,and aligning entities using a global matrix,the method accurately captures the relationships between overlapping entities and effectively maps them to the correct entity pairs,improving the accuracy of extraction results.Experiments conducted on the model using the China AI and Law Challenge(CAIL)dataset demonstrate that the model outperforms other compared models in terms of accuracy(85.21%),recall(81.19%),and F1 score(83.15%).In particular,the proposed model achieves an F1 score of 81.45%for single overlapping entities,and an F1 score of 80.67%for multiple overlapping entities.The experimental results show that the proposed LPRGC model significantly improves the accuracy of entity and relation extraction compared to existing methods,proving its effectiveness in enhancing model performance and addressing the issue of overlapping entities in complex legal texts.