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基于多特征融合和问答范式的生物医学事件论证检测方法.

Jinghan Tian1, Shuai Xing1, Qianmin Su1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, PR China.

Heliyon
|August 15, 2024
PubMed
概括

本研究引入了一种用于生物医学事件提取的新方法,通过使用多特征融合和问答方法来改进从文本中提取信息,以克服当前技术的局限性.

科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 计算生物学 计算生物学

背景情况:

  • 生物医学文本数据的快速扩张给信息提取带来了挑战.
  • 现有的事件参数检测方法与不相关的信息和深层次的语义理解作斗争.
  • 从复杂的生物医学文本中提取多个事件仍然很困难.

研究的目的:

  • 为生物医学文本开发先进的事件参数检测方法.
  • 从复杂的生物医学数据中提取有价值的信息,以提高准确性.
  • 解决目前不相关论证干扰和语义关联的方法的局限性.

主要方法:

  • 一种使用多特征融合和问题答案范式的新型事件参数检测方法.
  • 将事件分成问题答案格式,以简化检测复杂性.
  • 使用语法距离和先前知识来识别参数模板,减少无关的参数干扰.
  • 整合多功能注意力机制以捕捉深层次的语义特征.
  • 使用预定义事件结构的后处理来生成最终的生物医学事件.

主要成果:

  • 拟议的模型在MLEE数据集上获得了62.50%的F1事件提取得分.
  • 这种性能超过了现有的先进事件提取方法.
关键词:
生物医学事件提取多功能的多功能.问题答案范式的问题-答案范式.

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结论:

  • 开发的方法在生物医学事件提取方面表现出强的表现.
  • 它有效地支持从生物医学文本中挖掘有价值的信息.