# Biomedical named entity recognition using BERT in the machine reading comprehension framework

arXiv: https://arxiv.org/abs/2009.01560 · HTML: https://mlsanity.com/paper/2009.01560/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 22 of 22)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models](https://mlsanity.com/paper/2105.00827/) (2021)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension](https://mlsanity.com/paper/2303.08262/) (2023)
- [Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis](https://mlsanity.com/paper/2403.16303/) (2024)
- [From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2310.08365/) (2023)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
- [Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization](https://mlsanity.com/paper/2305.13066/) (2023)
- [Can We Edit LLMs for Long-Tail Biomedical Knowledge?](https://mlsanity.com/paper/2504.10421/) (2025)
- [SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs](https://mlsanity.com/paper/2505.14976/) (2025)
- [Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension](https://mlsanity.com/paper/2308.06454/) (2023)
- [AlbNER: A Corpus for Named Entity Recognition in Albanian](https://mlsanity.com/paper/2309.08741/) (2023)
- [Entity Recognition from Colloquial Text](https://mlsanity.com/paper/2401.04853/) (2024)
- [Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature](https://mlsanity.com/paper/2401.07447/) (2024)
- [Better than BERT but Worse than Baseline](https://mlsanity.com/paper/2105.05915/) (2021)
- [NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension](https://mlsanity.com/paper/2305.03970/) (2023)
- [Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization](https://mlsanity.com/paper/2305.14645/) (2023)
- [Named Clinical Entity Recognition Benchmark](https://mlsanity.com/paper/2410.05046/) (2024)
- [RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records](https://mlsanity.com/paper/2507.15867/) (2025)
- [Effective Multi-Task Learning for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2507.18542/) (2025)
- [Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER](https://mlsanity.com/paper/2510.22285/) (2025)

## Abstract (arXiv)

Recognition of biomedical entities from literature is a challenging research focus, which is the foundation for extracting a large amount of biomedical knowledge existing in unstructured texts into structured formats. Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method. This method, however, often cannot take full advantage of the semantic information in the dataset, and the performance is not always satisfactory. In this work, instead of treating the BioNER task as a sequence labeling problem, we formulate it as a machine reading comprehension (MRC) problem. This formulation can introduce more prior knowledge utilizing well-designed queries, and no longer need decoding processes such as conditional random fields (CRF). We conduct experiments on six BioNER datasets, and the experimental results demonstrate the effectiveness of our method. Our method achieves state-of-the-art (SOTA) performance on the BC4CHEMD, BC5CDR-Chem, BC5CDR-Disease, NCBI-Disease, BC2GM and JNLPBA datasets, achieving F1-scores of 92.92%, 94.19%, 87.83%, 90.04%, 85.48% and 78.93%, respectively.