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.
ArXiv
|July 28, 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,用于生物医学关系提取. 通过结合不同的数据集,BioREx显著提高了性能,在BioRED数据库中取得了最先进的结果.
科学领域:
- 生物医学自然语言处理 (NLP)
- 提取信息 提取信息
- 计算生物学 计算生物学
背景情况:
- 生物医学关系提取 (RE) 对于基于文献的发现和知识图的构建至关重要.
- 目前的RE模型受限于小型的,域特定的数据集和昂贵的手动注释.
- 由于数据异质性,开发通用和高性能RE模型具有挑战性.
结论:
- 拟议的框架和BioREx方法通过利用组合数据集,有效地克服了现有的RE方法的局限性.
- 这项工作通过为关系提取提供可扩展和高性能解决方案来推进生物医学NLP.
- 开发的工具和数据集为加速生物医学研究和知识发现提供了巨大的潜力.
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