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探索医学自然语言处理在多种语言中的最新亮点:一项调查

Anastassia Shaitarova¹, Jamil Zaghir^2,3, Alberto Lavelli⁴

¹Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

Yearbook of medical informatics

|December 26, 2023

概括

这项调查突出了生物医学和临床自然语言处理 (NLP) 对除英语以外的语言的进展. 虽然变压器模型和数据集正在增加,但医疗NLP中低资源语言需要更多的资源.

科学领域:

生物医学信息学生物医学信息学
计算语言学计算语言学
自然语言处理自然语言处理.

背景情况:

生物医学和临床自然语言处理 (NLP) 研究越来越多地扩展到英语之外.
多语言NLP在数据可用性和模型开发方面提出了独特的挑战.

研究的目的:

在非英语 (LoE) 语言中调查生物医学和临床NLP的现状.
专注于LOE中的数据资源,语言模型和常见的NLP任务.
确定多语言医学NLP的研究缺口和未来的机会.

主要方法:

2020-2022年临床和生物医学NLP出版物的文献综述.
专注于多语言和 LoE 挑战.
数据库查询和手动选择相关研究,并补充了现有的审查论文.

主要成果:

基于变压器的语言模型越来越多地用于LoE的医疗NLP任务.
对于临床NLP在LoE,特别是欧洲语言的注释数据集已经增长.
常见的任务包括信息提取,命名实体识别和否定检测.
对低资源语言量身定制的数据集和模型的需求仍然存在.

结论:

显然,医学NLP在英语以外的语言方面取得了显著的进展.
有机会为代表性不足的语言开发专门的资源.
需要进一步的研究来解决多语言医疗文本处理的独特挑战.

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