Chinese Medical Named Entity Recognition Based on Pre-training Model

Named Entity Recognition (NER) task aims to identify named entities from unstructured text and classify them into corresponding entity types. Existing pretraining models typically utilize BERT models to learn word embeddings at the character level, disregarding the semantic relationships between phrases. They also pay less attention to long-distance dependencies within sentences. Additionally, the datasets suffer from challenges such as small scale, lack of standardization, and annotation errors, all of which contribute to poor model robustness. Therefore, this paper proposes a Chinese medical named entity recognition model based on RoBERTa (A Robustly Optimized BERT Pre-training Approach), adversarial training, and hybrid encoding layers to enhance semantic understanding and model generalization. The proposed model is evaluated on three real clinical datasets. And experimental results demonstrate significant performance improvement compared to the baseline models. Furthermore, the advantages of this proposed approach over the baseline models are further analyzed through experiments.