现代信息科技2024，Vol.8Issue(24)：44-48,53,6.DOI:10.19850/j.cnki.2096-4706.2024.24.010
基于LERT预训练模型的医疗搜索意图识别研究
Research on Medical Search Intention Recognition Based on LERT Pre-trained Model
摘要
Abstract
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%.关键词
信息系统/意图识别/自然语言处理/大语言模型/医疗搜索Key words
information system/intention recognition/Natural Language Processing/Large Language Model/medical search分类
信息技术与安全科学引用本文复制引用
曾嘉慧,田晓琼,刘超毅..基于LERT预训练模型的医疗搜索意图识别研究[J].现代信息科技,2024,8(24):44-48,53,6.基金项目
湖南省自然科学基金资助项目(2024JJ8206) （2024JJ8206）
