Generative named entity recognition framework for Chinese legal domain

Abstract

Named entity recognition (NER) is a crucial task in natural language processing, particularly challenging in the legal domain due to the intricate and lengthy nature of legal entities. Existing methods often struggle with accurately identifying entity boundaries and types in legal texts. To address these challenges, we propose a novel sequence-to-sequence framework designed specifically for the legal domain. This framework features an entity-type-aware module that leverages contrastive learning to enhance the prediction of entity types. Additionally, we incorporate a decoder with a copy mechanism that accurately identifies complex legal entities without the need for explicit tagging schemas. Our extensive experiments on two legal datasets show that our framework significantly outperforms state-of-the-art methods, achieving notable improvements in precision, recall, and F1 score. This demonstrates the effectiveness of our approach in improving entity recognition in legal texts, offering a promising direction for future research in legal NER.

Keywords

• Named entity recognition
• Legal text
• Contrastive learning
• Multi-task training