在西班牙语单语言和跨语言自动残疾注释方面的进展
Iakes Goenaga1, Edgar Andres2, Koldo Gojenola3
1HiTZ: Basque Center for Language Technology, University of the Basque Country UPV/EHU, Donostia, Spain.
BMC bioinformatics
|June 26, 2023
概括
这项研究通过结合深度学习模型,在西班牙医学文本中推进了自动残疾注释. 结果显示,提高了准确性和跨语言传输的潜力,解决了数据稀缺问题.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 在医学NLP中,自动识别残疾落后于疾病识别.
- 缺少注释的 corpora 阻碍了残疾注释的进展.
- 神经网络可以在序列标记的表示类型之间进行翻译.
研究的目的:
- 展示单语言 (西班牙语) 和跨语言 (英语-西班牙语) 自动残疾注释方面的进展.
- 为了识别西班牙生物医学摘要中的残疾提及.
- 提高残疾注释系统的准确性和覆盖率.
主要方法:
- 结合深度学习模型,具有不同的嵌入细分度,用于序列对序列的标记.
- 集成了一个缩写和缩写检测模块来增强覆盖范围.
- 使用神经架构进行自动注释任务.
主要成果:
- 结合不同的词嵌入表示的单语言实验优于单个表示.
- 在西班牙残疾注释中取得了最先进的结果.
- 英语和西班牙语之间的跨语言转移 (零射击) 显示出有希望的结果.
结论:
- 结合多种不同的词嵌入,显著提高了西班牙语残疾注释的准确性.
- 跨语言转移学习为残疾注释中的数据稀缺提供了可行的解决方案.
- 开发的方法推动了医疗NLP在残疾识别领域的发展.
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