生物医学关系提取与知识库精细化的弱监督
Wonjin Yoon1, Sean Yi1, Richard Jackson2
1Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea.
Database : the journal of biological databases and curation
|August 8, 2023
概括
这项研究引入了一种新的生物医学关系提取系统,使用弱监督来克服数据限制. 精细的弱监督方法显著改善了性能,在BioCreative VII挑战赛中获得了第三名.
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:
- 生物医学关系提取 (BioRE) 对于理解生物医学文献至关重要.
- 监督学习和大型语言模型 (LLM) 主导着BioRE,但需要大量的人类注释数据.
- 数据稀缺性和注释成本限制了当前BioRE系统的性能和覆盖范围.
研究的目的:
- 为BioCreative VII挑战-DrugProt赛道开发一个BioRE系统.
- 通过利用弱监督来解决BioRE中人类注释数据的局限性.
- 通过新的弱监管策略,提高BioRE系统的性能.
主要方法:
- 开发了一个BioRE系统,将语言模型结构与弱监督相结合.
- 通过训练现有数据的模型来预测外部文献上的标签 (模型标签数据集) 创建了一个弱标签的数据集.
- 使用外部知识库对模型标记数据集进行了改进,然后对人类标记数据进行了微调.
主要成果:
- 使用精细弱监督的方法与仅在人类标记数据上训练的模型相比,显示出显著的性能提升.
- 开发的系统在BioCreative VII挑战-DrugProt赛道中获得了第三名.
- 软弱的监督,当通过外部知识来改进时,在提高BioRE性能方面被证明是有效的.
结论:
- 精细的弱监督是克服生物医学关系提取数据局限性的可行和有效策略.
- 拟议的系统为开发高性能BioRE工具提供了一个有希望的方向.
- 这项工作强调了将模型生成的标签与BioRE的外部知识库相结合的潜力.
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