生物REx:通过利用异质数据集来改善生物医学关系提取
Po-Ting Lai1, Chih-Hsuan Wei1, Ling Luo2
1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), MD, 20894 Bethesda, USA.
Journal of biomedical informatics
|September 6, 2023
概括
本研究介绍了BioREx,这是一个新的框架和以数据为中心的方法,通过结合不同的数据集,显著改善了生物医学关系提取. 生物REx实现了最先进的性能,并增强了各种生物医学NLP任务的模型通用性.
科学领域:
- 生物医学自然语言处理 (NLP)
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 生物医学关系提取 (RE) 识别文本中的生物医学实体之间的关系.
- 由于昂贵的手动注释,当前的RE方法在小的,域特定的数据集上扎.
- 这限制了通用和高性能RE模型的开发.
研究的目的:
- 提出一个新的框架,系统地解决生物医学RE数据集中的数据异质性.
- 开发BioREx,一种以数据为中心的方法,使用联合的大规模数据集来提取关系.
- 提高生物医学RE模型的性能和通用性.
主要方法:
- 开发了一个框架,将异构的生物医学RE数据集集成到统一的,大规模的数据集中.
- 实施了BioREx,这是一个以数据为中心的方法,利用组合数据集来提取关系.
- 与基准系统和最先进的方法 (如转移和多任务学习) 相比,评估了BioREx.
主要成果:
- 在BioRED集体上,BioREx获得了79.6%的最新F1得分,超过了5.2%的基准.
- 综合数据集在五个不同的RE任务中提高了性能.
- 与转移学习和多任务学习相比,BioREx表现出良好的表现,并在未见任务上表现出强度.
结论:
- 拟议的框架和BioREx有效地解决了生物医学RE的数据限制.
- 生物雷克斯提供了一个强大的和可通用的解决方案,推进生物医学NLP领域.
- 综合数据集和BioREx工具是公开的,以促进进一步的研究.
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