  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.
