YNU-ISE-ZXW at ROCLING 2023 MultiNER-Health Task: A Transformer-based Model with LoRA for Chinese Healthcare Named Entity Recognition

YNU-ISE-ZXW at ROCLING 2023 MultiNER-Health Task: A Transformer-based Model with LoRA for Chinese Healthcare Named Entity Recognition

Named entity recognition (NER) is a subtask in the field of information extraction in natural language processing (NLP). Its main goal is to recognize named entities in text and classify them into predefined categories. In the medical field, NER technology is used to automatically identify medical-related entities, such as symptoms, examinations, diseases, and drugs, so that medical staff can better treat patients. For the named entity recognition task in the medical field proposed by ROCLING 2023, we built three models based on Transformers and used technologies such as Focal Loss and LoRA. We conducted comparative experiments on the development set and the test set, and found that the effects of the three models were not much different. Finally, our submitted DeBERTa model named RUN3 achieved a macro-f1 score of 67.79, ranking 5th.