Abstract
Nested named entity recognition (NNER), a subtask of named entity recognition (NER), aims to recognize more types of entities and complex nested relationships, presenting challenges for real-world applications. Traditional methods, such as sequence labeling, struggle with the task because of the hierarchical nature of these relationships. Although NNER methods have been extensively studied in various languages, research on Chinese NNER (CNNER) remains limited, despite the complexity added by ambiguous word boundaries and flexible word usage in Chinese. This paper proposes a multi-scale transformer prototype network (MSTPN)-based CNNER method. Multi-scale bounding boxes for entities are deployed to identify nested named entities, transforming the recognition of complex hierarchical entity relationships into a more straightforward task of multi-scale entity bounding box recognition. To improve the accuracy of multi-scale entity bounding box recognition, MSTPN, leverages the sequence feature extraction capabilities of transformers and utilizes the advantages of prototype networks in few-shot and multiple-category tasks. A distance-based multi-bounding box cross-entropy loss method is introduced to optimize MSTPN, ensuring the coordinated optimization of transformer and prototype parameters. Experiments using the ACE05, ChiNesE, and RENMIN datasets demonstrate that MSTPN outperforms state-of-the-art methods, highlighting the effectiveness of prototype networks in natural language processing tasks involving long sequences.