Chinese Medical Named Entity Recognition Based on Gated Attention Unit

The medical named entity recognition aims to automatically identify and classify medical entities in electronic medical records, which plays a very important role in downstream tasks such as information retrieval and knowledge graph. Existing methods usually ignore the dependencies between entities. To address this, this paper proposes a gated attention unit-based model for Chinese medical named entity recognition. First, the proposed model uses the pre-training model MC-BERT to capture contextual information. Then, it uses the cross-attention and gated attention unit to enhance the interaction between entity query and contextual semantics, and further extract the dependency and correlation between entities. Finally, the proposed model uses the matching algorithm of bipartite graph to calculate the loss. This paper conducted experiments on three datasets, including the CMeEE, CMQNN, and MSRA. The experimental results show that the F₁ values of the proposed model on the three datasets are 70.74%, 96.92%, and 95.53%, respectively, which outperforms other related models, demonstrating the effectiveness of the proposed model in Chinese medical named entity recognition task.