RT Journal Article
SR Electronic
T1 Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.15.460567
DO 10.1101/2021.09.15.460567
A1 Dao-Ling Huang
A1 Quanlei Zeng
A1 Yun Xiong
A1 Shuixia Liu
A1 Chaoqun Pang
A1 Menglei Xia
A1 Ting Fang
A1 Yanli Ma
A1 Cuicui Qiang
A1 Yi Zhang
A1 Yu Zhang
A1 Hong Li
A1 Yuying Yuan
YR 2021
UL http://biorxiv.org/content/early/2021/09/17/2021.09.15.460567.abstract
AB A combined high-quality manual annotation and deep-learning natural language processing study is reported to make accurate name entity recognition (NER) for biomedical literatures. A home-made version of entity annotation guidelines on biomedical literatures was constructed. Our manual annotations have an overall over 92% consistency for all the four entity types — gene, variant, disease and species —with the same publicly available annotated corpora from other experts previously. A total of 400 full biomedical articles from PubMed are annotated based on our home-made entity annotation guidelines. Both a BERT-based large model and a DistilBERT-based simplified model were constructed, trained and optimized for offline and online inference, respectively. The F1-scores of NER of gene, variant, disease and species for the BERT-based model are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those for the DistilBERT-based model are 95.14%, 86.26%, 91.37% and 89.92%, respectively. The F1 scores of the DistilBERT-based NER model retains 97.8%, 92.2%, 98.7% and 93.9% of those of BERT-based NER for gene, variant, disease and species, respectively. Moreover, the performance for both our BERT-based NER model and DistilBERT-based NER model outperforms that of the state-of-art model—BioBERT, indicating the significance to train an NER model on biomedical-domain literatures jointly with high-quality annotated datasets.Competing Interest StatementThe authors have declared no competing interest.