YNU-HPCC at ROCLING 2023 MultiNER-Health Task: A transformer-based approach for Chinese healthcare NER

YNU-HPCC at ROCLING 2023 MultiNER-Health Task: A transformer-based approach for Chinese healthcare NER

Chinese healthcare NER is an essential task in natural language processing to automatically identify healthcare entities such as symptoms, chemicals, diseases, and treatments for machine reading and understanding. Previous studies used Bi-directional Long Short-Term Memory (BiLSTM) and Conditional Random Fields (CRF) to solve NER tasks. This paper uses the RoBERTa-large pre-trained language model combined with BiLSTM-CRF to build a NER model suitable for Chinese healthcare tasks. Dropout is used to improve the performance and stability of the model, and gradient clipping is added to prevent gradient explosion. Comparative experiments were conducted on the dev set to select the model with the best performance for submission. The best model managed to achieve a macro-averaging F1 score of 68.40, which ranked second in the ROCLING 2023 shared task.