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Biomedical named entity recognition (BNER) serves as the foundation fornumerous biomedical text mining tasks. Unlike general NER, BNER require acomprehensive grasp of the domain, and incorporating external knowledge beyondtraining data poses a significant challenge. In this study, we propose a novelBNER framework called DMNER. By leveraging existing entity representationmodels SAPBERT, we tackle BNER as a two-step process: entity boundary detectionand biomedical entity matching. DMNER exhibits applicability across multipleNER scenarios: 1) In supervised NER, we observe that DMNER effectivelyrectifies the output of baseline NER models, thereby further enhancingperformance. 2) In distantly supervised NER, combining MRC and AutoNER as spanboundary detectors enables DMNER to achieve satisfactory results. 3) Fortraining NER by merging multiple datasets, we adopt a framework similar toDS-NER but additionally leverage ChatGPT to obtain high-quality phrases in thetraining. Through extensive experiments conducted on 10 benchmark datasets, wedemonstrate the versatility and effectiveness of DMNER.

DMNER: Biomedical Entity Recognition by Detection and Matching