基于TinyBERT-CNN融合模型的"关于发烧性疾病的论文"中的问题的意图分类方法
Helong Yu1, Chunliu Liu1, Lina Zhang1
1College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
Computers in biology and medicine
|June 5, 2023
概括
一个新的TinyBERT-CNN模型在经典的"关于发烧性疾病的论文"中有效地分类了问题的意图. 这种方法解决了传统中医药问答系统的短暂,有限和不平衡数据的挑战.
科学领域:
- 传统中国医药 传统中国医药
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- "关于发烧性疾病的论文"是中国医学材料的基础文本.
- 本文中的知识地图支持传统中医 (TCM) 问答系统.
- 意图分类对于TCM问答系统至关重要,但缺乏对这一经典文本的具体研究.
研究的目的:
- 开发一个有效的意图分类模型的TCM问题,基于"治疗发烧性疾病".
- 解决现有模型的局限性,以解决这个领域中常见的短,稀缺和不平衡的数据集.
主要方法:
- 提出了一个TinyBERT-CNN模型,将双向变压器编码器与卷积神经网络相结合.
- 使用TinyBERT进行文本嵌入和编码,以捕获全球矢量信息.
- 雇佣CNN使用编码特征进行最终意图分类.
主要成果:
- TinyBERT-CNN模型在准确性 (96.4%),回忆 (95.9%) 和F1得分 (96.2%) 中取得了很高的表现.
- 与特定数据集上的其他模型相比,表现出优异的性能.
- 在"治疗发烧性疾病"的背景下有效地分类了问题的意图.
结论:
- 提议的TinyBERT-CNN模型是有效的问题意图分类在"治疗发烧性疾病".
- 为开发一个全面的TCM问答系统提供必要的技术支持.
- 突出了先进的NLP模型分析经典医学文本的潜力.
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