化学-蛋白质关系提取与预先训练的快速调
Jianping He1, Fang Li1, Xinyue Hu1
1School of Biomedical Informatics UTHealth Houston, USA.
概括
预先训练的提示调可以增强化学-蛋白质关系提取. 这种方法提高了预训练语言模型 (PLM) 构建生物医学知识图的效率.
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 (NLP) 是一种自然语言处理.
背景情况:
- 生物医学关系提取对于创建知识图和数据库至关重要.
- 预训练的提示调整是一种有前途的NLP范式,它为预训练的语言模型 (PLM) 重构任务.
研究的目的:
- 应用预训练的提示调到化学-蛋白质关系提取.
- 在BioCreative VI CHEMPROT数据集上评估其有效性.
主要方法:
- 使用预先训练的提示调整来将化学蛋白关系提取转换为一个掩盖的语言问题.
- 使用预先训练有素的语言模型 (PLM) 来解决重构的任务.
主要成果:
- 预先训练的提示调在化学蛋白相互作用分类中显著超过了基线方法.
- 在这个特定的NLP任务中,PLM的效率得到了提高.
结论:
- 快速调整是增强化学蛋白关系提取的有效策略.
- 这种方法为PLM在构建生物医学知识图表时提供了更高的效率.
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