关键词: 命名实体识别, 小样本学习, 提示学习, 度量学习, 统一医学语言系统知识注入

Abstract: The Chinese medical Named Entity Recognition (NER) task aims to extract medical entities from unstructured medical texts and assign these entities to predefined medical entity categories. Addressing the challenges of sample scarcity, semantic diversity, and cross-contextual distribution differences in Chinese medical NER, a unified framework integrating prompt learning and metric learning was proposed, called Prompted-by-Synonyms cross-set-aligned Uncertainty-Guided aggregation (PS-UG). First, a synonym-aware interrogative prompt template was constructed based on the Unified Medical Language System (UMLS) to explicitly expose label semantics and improve category separability. Second, a bidirectional cross-set attention and gating fusion mechanism was designed to achieve accurate semantic alignment between the support set and the query set. Finally, word-level representations were mapped to a diagonal Gaussian distribution, and symmetric Kullback-Leibler (KL) divergence is used as a metric for consistency between training and inference for category discrimination. Experiments were conducted on the CCKS2019, IMCS-V2-NER medical datasets, and CLUENER2020 general dataset using 5-way 1~2-shot and 5-way 5~10-shot methods. The experimental results show that PS-UG achieves significant improvements over the Prompt-based Metric Learning (ProML) baseline, with improvements of 8.99 percentage points and 7.38 percentage points on the CCKS2019 dataset, 5.03 percentage points and 4.24 percentage points on the IMCS-V2-NER dataset, and 6.14 percentage points and 2.24 percentage points on the CLUENER2020 dataset.

Key words: Named Entity Recognition (NER), few-shot learning, prompt learning, metric learning, Unified Medical Language System (UMLS) knowledge injection