对不同样式的临床叙述进行微调核心参考分辨率
Yuxiang Liao1, Hantao Liu1, Irena Spasić1
1School of Computer Science and Informatics, Cardiff University, United Kingdom.
Journal of biomedical informatics
|December 20, 2023
概括
对临床文本的微调核心参考分辨率 (CR) 模型具有挑战性. 一种带有持续培训的半自动注释方法为在低资源环境中快速转移模型提供了实际解决方案.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 核心参考解析 (CR) 对于下游的NLP任务,如总结和信息提取至关重要.
- 现有的开源CR工具在临床领域表现不佳.
研究的目的:
- 在临床数据上探索微调CR模型的实际解决方案.
- 为了解决临床领域当前CR工具的低标准性能.
主要方法:
- 使用整体方法研究了自动白银标准生成.
- 提出了一种半自动注释过程,以帮助手动注释.
- 积极学习与随机抽样进行了比较,以实现成本效益高的手动注释.
主要成果:
- 自动白银标准生成对于CR模型微调无效.
- 积极学习需要谨慎的应用.
- 半自动注释与持续培训相结合,在低资源场景中促进了快速CR模型转移.
- 综合方法显示了提高准确性的潜力.
结论:
- 成功将一般CR模型转移到临床领域.
- 提供了各种临床叙述中快速CR模型转移的实际建议.
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