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

• URL: http://arxiv.org/abs/2009.01560v2
• Date: Mon, 17 May 2021 07:48:41 GMT
• Title: Biomedical named entity recognition using BERT in the machine reading comprehension framework
• Authors: Cong Sun, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang
• Abstract summary: We propose a new method to implement biomedical named entity recognition (BioNER) Instead of treating the BioNER task as a sequence labeling problem, we formulate it as a machine reading comprehension problem. Our method achieves state-of-the-art (SOTA) performance on the BC4CHEMD, BC5CDR-Chem, BC5CDR-Disease, NCBI-Disease, BC2GM and JNLPBA datasets.
• Score: 16.320249089801884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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