Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.

翻译：生物医学命名实体识别（BNER）是众多生物医学文本挖掘任务的基础。与通用NER不同，BNER要求对领域有全面理解，而将训练数据之外的外部知识融入其中是一项重大挑战。在本研究中，我们提出一种名为DMNER的新型BNER框架。通过利用现有实体表示模型SAPBERT，我们将BNER处理为两步过程：实体边界检测与生物医学实体匹配。DMNER展现出在多种NER场景中的适用性：1）在有监督NER中，我们观察到DMNER能有效纠正基线NER模型的输出，从而进一步提升性能。2）在远程监督NER中，将MRC与AutoNER结合作为跨度边界检测器，使DMNER能够获得令人满意的结果。3）在通过合并多个数据集训练NER时，我们采用类似DS-NER的框架，但额外利用ChatGPT获取训练中的高质量短语。通过在10个基准数据集上进行的大量实验，我们证明了DMNER的多功能性和有效性。