Motivation: Named Entity Recognition (NER) is a key task to support
biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining
high-quality expert annotated data is laborious and expensive, leading to the
development of automatic approaches such as distant supervision. However,
manually and automatically generated data often suffer from the unlabeled
entity problem, whereby many entity annotations are missing, degrading the
performance of full annotation NER models. Results: To address this problem, we
systematically study the effectiveness of partial annotation learning methods
for biomedical entity recognition over different simulated scenarios of missing
entity annotations. Furthermore, we propose a TS-PubMedBERT-Partial-CRF partial
annotation learning model. We harmonize 15 biomedical NER corpora encompassing
five entity types to serve as a gold standard and compare against two commonly
used partial annotation learning models, BiLSTM-Partial-CRF and EER-PubMedBERT,
and the state-of-the-art full annotation learning BioNER model PubMedBERT
tagger. Results show that partial annotation learning-based methods can
effectively learn from biomedical corpora with missing entity annotations. Our
proposed model outperforms alternatives and, specifically, the PubMedBERT
tagger by 38% in F1-score under high missing entity rates. The recall of entity
mentions in our model is also competitive with the upper bound on the fully
annotated dataset.

本文探讨了应用部分标注学习方法在缺失生物医学命名实体注释的场景中进行实体识别的有效性，并提出了一个新的 TS-PubMedBERT-Partial-CRF 模型来提高实体识别性能。实验结果表明，该模型在高缺失实体比例下的 F1-score 比使用全标注学习模型 PubMedBERT Tagger 高 38％，同时其实体识别的召回率也达到了与全标注学习数据集上的上界相竞争的水平。