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一种基于学习的度量方法,用于连接生物医学实体.

Ngoc D Le1,2, Nhung T H Nguyen3

  • 1Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam.

Frontiers in research metrics and analytics
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的基于指标的学习方法,用于生物医学实体链接,有效处理不平衡的数据集并降低计算成本,以改善提及实体表示. 该方法确保了与最先进的模型相比具有竞争力的性能.

关键词:
生物医学实体链接链接实体的规范化 实体的规范化不平衡的数据不平衡的数据.计量学学习学习的方法软半径聚类软半径聚类三倍损失的三倍损失.

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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.
  • 机器学习 机器学习

背景情况:

  • 生物医学实体链接将文本提及与知识库概念 (例如,UMLS) 连接起来.
  • 挑战包括提及模糊性和多样化的实体表达式.
  • 现有的基于BERT的方法与不平衡的生物医学数据集作斗争,在这种情况下,下方采样阻碍了学习.
  • 减少样本减少了模型学习上下文提及表示的能力.

研究的目的:

  • 为生物医学实体链接提出基于指标的学习方法,以解决数据不平衡问题.
  • 开发一种技术,以整体的方式对待实体及其提及,无论提及频率如何.
  • 改进在不平衡数据集中提及和实体表示的学习.

主要方法:

  • 一种使用三倍损失的基于指标的学习方法.
  • 集成一个集群技术来学习表示.
  • 在培训期间将实体及其提及视为一个统一的整体.
  • 对MedMentions和BC5CDR数据集的评估.

主要成果:

  • 成功地解决了生物医学实体链接中数据不平衡的挑战.
  • 与最先进的模型相比,实现了竞争性性能.
  • 在训练和推断过程中显著降低了计算成本.
  • 证明提及和实体表示的有效学习.

结论:

  • 拟议的基于指标的学习方法为不平衡的生物医学实体链接数据集提供了有效的解决方案.
  • 这种方法提高了模型的性能和效率.
  • 该方法为传统的下方采样技术提供了强大的替代方案.