A Chinese Few-Shot Named-Entity Recognition Model Based on Multi-Label Prompts and Boundary Information
Cong Zhou, Baohua Huang, Yunjie Ling · Applied Sciences · 2025
Currently, few-shot setting and entity nesting are two major challenges in named-entity recognition (NER). Compared to English, Chinese NER not only has issues such as complex grammatical structures, polysemy, and entity nesting but also faces low-resource scenarios in specific domains due to difficulties in sample annotation. To address these two issues, we propose a Chinese few-shot named-entity recognition model that integrates multi-label prompts and boundary information (MPBCNER). This model is an improvement based on a pre-trained language model (PLM) combined with a pointer network. First, the model uses multiple entity label words and position slots as prompt information in the entity recognition training task. Activating the relevant parameters in PLM associated with the corresponding entity labels through the prompt information improved the model’s performance in entity recognition under small-sample data. Secondly, by using a Graph Attention Network (GAT) to construct the boundary information extraction module, we integrated boundary information with text features, allowing the model to pay more attention to features near the boundaries when recognizing entities, thereby improving the accuracy of entity boundary recognition. Experiments on multiple public small-sample datasets and our own annotated datasets in the field of government auditing demonstrated the effectiveness of this model.