基于三重提示和对比学习的中文医疗命名实体识别OA

To address the issues of insufficient pretrained knowledge representation,ambiguous boundaries,and category confusion in Chinese medical few-shot named entity recognition,a multi-stage recognition approach guided by triple prompts was proposed in this study.Firstly,a triple-prompt learning mechanism was constructed,in which a stepwise reasoning paradigm,comprising boundary localization,type discrimination,and contextual verification was adopted.By integrating the interpretability of discrete templates with the optimizability of continuous vectors,the capability of the model to parse entity structures was enhanced.Secondly,a bidirectional gating network was designed to dynamically regulate feature interactions between prompt information and textual representations,there-by strengthening semantic capture for complex terminology.Finally,contrastive learning was introduced to optimize the distribution of the feature space,improving intra-class compactness and inter-class separability.Experimental results indicated that F1 scores of 91.86%,84.06%,and 88.93%were achieved on the IMCS-V2-NER,cMedQANER,and CCKS2019 datasets,respectively.These findings demonstrated that the proposed method con-sistently improved entity recognition performance and exhibited strong generalization capability under low-resource settings.