A Named Entity Recognition Method Based on Knowledge Distillation and Efficient GlobalPointer for Chinese Medical Texts

The task of named entity recognition has been widely used in medical text analysis, but there is still the problem of poor transfer ability in practical applications. This work proposes a novel named entity recognition method based on a proposed knowledge distillation framework and Efficient GlobalPointer for Chinese biomedical and clinical data. Specifically, our study leverages the Efficienct GlobalPointer to address the issue of entity nesting and introduces a context shield window to mitigate interference from redundant information. Furthermore, the model’s generalization ability is enhanced through a novel knowledge distillation framework. The proposed knowledge distillation framework solves the problem of independent feature learning process in feature distillation by using linkage mechanism. The recognition accuracy is improved by the proposed knowledge distillation method while keeping the model complexity low, so that our method can meet the inference speed requirement in real applications while ensuring a certain recognition accuracy. Our method achieves excellent experimental results on three publicly available Chinese datasets, where the comprehensive evaluation metric F1 exceeds the best results achieved by existing methods.