In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records. The challenge in medical NER arises from the complex nested structures and sophisticated medical terminologies, distinguishing it from its counterparts in traditional domains. In response to these complexities, we propose a medical NER model based on Machine Reading Comprehension (MRC), which uses a task-adaptive pre-training strategy to improve the model's capability in the medical field. Meanwhile, our model introduces multiple word-pair embeddings and multi-granularity dilated convolution to enhance the model's representation ability and uses a combined predictor of Biaffine and MLP to improve the model's recognition performance. Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical NER, demonstrate that our proposed model outperforms the compared state-of-the-art (SOTA) models.

提出了基于机器阅读理解（MRC）的医学命名实体识别（NER）模型，利用任务自适应预训练策略提高模型在医学领域的能力，并引入多词对嵌入和多粒度膨胀卷积以增强模型的表示能力，同时采用Biaffine和MLP的组合预测器来提高模型的识别性能。实验评估表明，我们提出的模型在中文嵌套医学NER的基准测试CMeEE上优于目前的最优模型。

基于MRC的嵌套式医学NER：共同预测与自适应预训练