Abstract
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.
Abstract (translated)
生物医学命名实体识别(BNER)是许多生物医学文本挖掘任务的基础。与一般命名实体识别不同,BNER需要对该领域进行全面的理解,而将训练数据以外的外部知识引入会产生巨大的挑战。在本研究中,我们提出了一种名为DMNER的新型BNER框架。通过利用现有的实体表示模型SAPBERT,我们处理BNER采用了两个步骤:实体边界检测和生物医学实体匹配。DMNER表现出适用于多种命名实体识别场景的能力:1)在监督BNER中,我们发现DMNER有效地修复了基线BNER模型的输出,从而进一步提高了性能。2)在远程监督BNER中,将MRC和AutoNER组合作为跨越边界检测器,使DMNER能够取得令人满意的结果。3)通过在10个基准数据集上开展广泛的实验,我们证明了DMNER的 versatility和有效性。
URL
https://arxiv.org/abs/2306.15736
