语音部分标记中的域名转移
Amila Kugic1, Stefan Schulz1, Markus Kreuzthaler1
1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
Studies in health technology and informatics
|May 17, 2025
概括
域名转移对临床自然语言处理中的机器学习产生了重大影响. 分析语言差异揭示了部分语音标签的变化,需要域调整以获得更好的性能.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 数据集分布的域名转移给机器学习模型带来了挑战.
- 临床自然语言处理 (NLP) 特别容易因为特定领域的语言变异而导致性能下降.
- 了解这些变化对于在医疗保健中开发强大的NLP工具至关重要.
研究的目的:
- 调查领域转移对临床NLP机器学习性能的影响.
- 分析跨临床叙述,生物医学摘要和新闻文章的语言差异.
- 识别有助于域移动的特定语言特征.
主要方法:
- 利用部分语音 (POS) 标签分布来量化语言差异.
- 在三个不同的文本领域中比较POS标签频率:临床叙述,生物医学摘要和新闻文章.
- 采用统计分析来识别标签发生的显著变化.
主要成果:
- 在分析的领域中观察到部分语音标签分布的显著变化.
- 与生物医学摘要和新闻文章相比,临床数据集显示未定义标签的频率更高.
- 这些语言差异突出了显著的领域转移,影响了NLP模型的概括.
结论:
- 这项研究证实了临床NLP数据集中的实质性领域转移,影响了模型的准确性.
- 临床数据中未定义的POS标签的普及凸显了对特定领域NLP解决方案的需求.
- 改进的领域适应技术和专门的工具对于提高临床NLP性能至关重要.
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