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.

本研究提出了一种新的生物医学命名实体识别框架 DMNER，其通过利用现有的实体表示模型 SAPBERT，将 BNER 分解为实体边界检测和生物医学实体匹配两个步骤，并表现出在不同 NER 情景下的适用性和有效性。

检测和匹配的生物医学实体识别

Biomedical Entity Recognition by Detection and Matching

Despite the widespread success of self-supervised learning via masked
language models (MLM), accurately capturing fine-grained semantic relationships
in the biomedical domain remains a challenge. This is of paramount importance
for entity-level tasks such as entity linking where the ability to model entity
relations (especially synonymy) is pivotal. To address this challenge, we
propose SapBERT, a pretraining scheme that self-aligns the representation space
of biomedical entities. We design a scalable metric learning framework that can
leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts.
In contrast with previous pipeline-based hybrid systems, SapBERT offers an
elegant one-model-for-all solution to the problem of medical entity linking
(MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking
datasets. In the scientific domain, we achieve SOTA even without task-specific
supervision. With substantial improvement over various domain-specific
pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining
scheme proves to be both effective and robust.

本研究提出了一种自我对齐的预训练模型 SapBERT，以处理医学名词实体链接（MEL）等实体级任务，利用 UMLS 等生物医学本体学的庞大集合，实现了新的最优结果，并证明其相对于 BioBERT 和 PubMedBERT 等先前的特定领域的 MLM 的有效性和鲁棒性。