通过有条件的独立性对临床文本进行上下文敏感的拼写纠正
Juyong Kim1, Jeremy C Weiss2, Pradeep Ravikumar1
1Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
概括
这项研究引入了一种用于临床拼写纠正的新型概率模型,即使使用有限的数据,也显著提高了准确性. 该方法利用深度字符级语言模型和腐败模型来有效识别医疗记录错误.
科学领域:
- 临床自然语言处理 临床自然语言处理
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 在临床医疗记录中经常出现拼写错误,这给自然语言处理带来了挑战.
- 标记临床数据集的有限可用性阻碍了开发有效的拼写纠正模型.
研究的目的:
- 开发一个临床拼写纠正的概率模型,需要最小的或不需要现实世界的数据.
- 解决临床文本机器学习模型训练中的数据稀缺问题.
主要方法:
- 开发了一个基于条件独立假设的概率模型.
- 该模型被模块化分解为语言模型和腐败模型.
- 一个深度的字符级语言模型被训练在一个大型的临床群体上,补充了一个基于编辑的腐败模型.
主要成果:
- 拟议的模型在医疗保健拼写纠正任务上显著优于现有的基线方法.
- 该模型即使具有很少或没有标记的真实世界临床数据,也显示出有效性.
结论:
- 开发的概率模型为临床拼写纠正提供了强大的解决方案,克服了数据限制.
- 这种方法提高了医疗记录处理的可靠性,并推进了临床自然语言处理.
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