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Semantic classification of Indonesian consumer health questions.

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从消费者健康论坛中提取句子,实体和关键词,使用多任务学习.

Tsaqif Naufal1, Rahmad Mahendra1, Alfan Farizki Wicaksono2

  • 1Faculty of Computer Science, Universitas Indonesia, Kampus UI, 16424, Depok, West Java, Indonesia.

Journal of biomedical semantics
|May 6, 2025
PubMed
概括

为健康论坛开发自然语言处理工具可以提高问题的理解. 多任务学习增强了医疗实体识别和关键词提取,帮助用户更有效地找到健康信息.

关键词:
消费者健康问题问答系统提取关键词短语的提取方法医疗实体认可 医疗实体认可多任务学习多任务学习句子的认可句子的认可

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科学领域:

  • 自然语言处理自然语言处理.
  • 医疗信息学 医疗信息学
  • 计算语言学 计算语言学

背景情况:

  • 在线健康论坛是有价值的信息来源,但在专业参与方面面临挑战.
  • 医疗保健专业人员的有限参与可能会推迟对用户健康查询的响应.
  • 具有高级问题处理的半自动系统可以提高论坛的有效性.

研究的目的:

  • 开发和评估用于处理与健康有关的问题的自然语言处理 (NLP) 模块.
  • 为了更好地理解,改进用户问题中的关键组件的识别.
  • 通过提取的关键信息来重新制定更有效的问题.

主要方法:

  • 扩展和公开印尼语文数据集的句子识别 (SR),医疗实体识别 (MER) 和关键词提取 (KE).
  • 使用基于变压器的模型建立基线,使用印尼语的九种编码器变体.
  • 建议和评估双向和三向配置的多任务学习 (MTL) 模型,并行和等级架构.

主要成果:

  • 建立了SR,MER和KE任务的注释者间协议.
  • 单任务学习 (STL) 显示了不同表现最好的模型,表明较大的模型并不总是优越的.
  • 配对的MTL模型在所有三个任务中都表现优于STL基线.
  • 三路MTL模型显示不一致的性能模式,一些配置改进了MER和KE,但没有SR.

结论:

  • 为SR,MER和KE任务创建了一个扩展的印尼数据集.
  • 基于变压器的模型在印尼语中为这些NLP任务建立了基线.
  • MTL方法表明,共享信息比SR更有利于MER和KE学习.