Research on Medical Text Named Entity Recognition Model Based on Prompt Contrastive Learning

Medical Named Entity Recognition (NER) aims to automatically identify entities like diseases, drugs, and symptoms from medical texts, supporting medical knowledge graphs, clinical decision-making, and intelligent systems. Current research relies on deep learning models, particularly pre-trained language models like BERT, improving recognition accuracy. Challenges include data sparsity, ambiguous boundaries, and synonym diversity, affecting generalization on specific datasets. To address this, we enhance BERT with prompt learning and contrastive learning, using learnable entity class embeddings and similarity computations to improve classification and recognition on few-shot datasets. Our model achieves F1 scores of 90.19% on the CCKS 2019 dataset and 83.30% on the JNLPBA dataset.