一个混合模型,整合了RoBERTa,TF-IDF和注意力机制,用于医学查询意图分类
Zejian Liang1, Yunxiang Zhao2, Haiwen Xu1
1Faculty of Science, Civil Aviation Flight University of China, Chendu, Sichuan, China.
Scientific reports
|November 29, 2025
概括
本研究引入了一种医疗查询意图分类的混合模型,通过将深度学习与传统方法相结合来提高准确性. 这种新的方法提高了检索相关性和知识增强,以便更好地获得医疗信息.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 准确的用户查询意图识别对于医疗检索系统至关重要.
- 现有的模型,如TF-IDF和词袋,无法捕捉上下文语义,而预先训练的语言模型可能会忽略关键字的重要性和类别优先级,影响长尾类别和通俗查询.
研究的目的:
- 开发一种混合模型,将深度语义理解的优势与关键字权重和类别特定信息相结合,以改进医疗意图分类.
主要方法:
- 开发了一个混合模型,将RoBERTa-wwm-ext与句子级和类别中心的TF-IDF集成在一起.
- 使用具有适应性注意力的三分支注意力融合机制,以动态加重深层和浅层特征.
- 分类中心向量被用作语义,以解决阶级不平衡问题.
主要成果:
- 拟议的模型在KUAKE-QIC数据集上实现了0.824的准确性和0.800的宏F1得分.
- 性能超过了所有基线模型的2.3%点.
- 该方法证明了深浅特征相互作用的协同效益,改善了主导类别的回忆力和少数类别的精度.
结论:
- 混合模型为医疗意图分类提供了可概括的框架,平衡语义完整性与关键词先验.
- 这种方法为临床问答,医学搜索排名和智能分拣系统提供了更可靠的基础.
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