Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study

Huang, D.-L.; Zeng, Q.; Xiong, Y.; Liu, S.; Pang, C.; Xia, M.; Fang, T.; Ma, Y.; Qiang, C.; Zhang, Y.; Li, H.; Yuan, Y.

doi:10.1101/2021.09.15.460567

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bioRxiv · 10.1101/2021.09.15.460567

Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study

Huang, D.-L.·Zeng, Q.·Xiong, Y.·Liu, S.·Pang, C.·Xia, M.·Fang, T.·Ma, Y.·Qiang, C.·Zhang, Y.·Li, H.·Yuan, Y.

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Abstract

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.

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Huang, D.-L., Zeng, Q., Xiong, Y., Liu, S., Pang, C., Xia, M., Fang, T., Ma, Y., Qiang, C., Zhang, Y., Li, H., Yuan, Y.. 2021-09-17. Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study. https://doi.org/10.1101/2021.09.15.460567

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