Abstract：Many biomedical literature mining systems use the pre-training language model BioBert which provides state-of-the-art biomedical named entity recognition after pre-training. However, BioBert is too large scale and slow. This paper presents a faster biomedical named entity recognition model, FastBioNER, that is based on knowledge distillation. FastBioNER compresses the BioBert model using dynamic knowledge distillation. A dynamic weight function is used to simulate the real learning behavior to adjust the importance of the loss function of each part during training. Then, the trained BioBert is compressed into a small student model by dynamic knowledge distillation. The FastBioNER model was validated on three common data sets, NCBI disease, BC5CDR-chem and BC4CHEMD. The tests show that FastBioNER had the highest F1 values after BioBert at 88.63%, 92.82% and 92.60% for the three data sets while reducing the BioBert model size by 39.26% and the inference time by 46.17% at the cost of 1.10%, 0.86% and 0.15% smaller F1.
胡滨, 耿天玉, 邓赓, 段磊. 基于知识蒸馏的高效生物医学命名实体识别模型[J]. 清华大学学报（自然科学版）, 2021, 61(9): 936-942.
HU Bin, GENG Tianyu, DENG Geng, DUAN Lei. Faster biomedical named entity recognition based on knowledge distillation. Journal of Tsinghua University(Science and Technology), 2021, 61(9): 936-942.
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