Research on Medical Search Intention Recognition Based on LERT Pre-trained Model
Correct identification of user intention can help improve the accuracy of medical search and provide convenience for users of medical search systems.In order to improve the accuracy of intention recognition in medical search,this paper uses the KUAKE-Query Intent Criterion dataset in the Chinese Biomedical Language Understanding Evaluation to fine-tune the LERT pre-trained model(Chinese-LERT-base)and the BERT pre-trained model(BERT-base-Chinese),and evaluates the intention classification accuracy of the fine-tuned model.The classification accuracy of the fine-tuned LERT model in the"treatment plan""disease description"and"etiological analysis"categories is improved by 4.53%,8%,and 8.34%,respectively,compared with the BERT model after fine-tuning,and the classification accuracy in the"other"category is reduced by 9.45%.The overall classification accuracy is improved by 0.22%.
information systemintention recognitionNatural Language ProcessingLarge Language Modelmedical search
国家科技期刊平台
NSTL
万方数据
