跨语言和多语言药物检测:基于变压器的分析分析
Lisa Raithel1,2,3, Johann Frei4, Philippe Thomas5
1Quality & Usability Lab, Technische Universität Berlin, Ernst-Reuter Platz 7, Berlin, 10587, Germany. raithel@tu-berlin.de.
BMC medical informatics and decision making
|October 2, 2025
概括
这项研究证明了使用变压器模型从医学文本中有效地提取多语言和跨语言的药物名称. 它表明,跨语言医学知识转移是可行的,在德语,英语,法语和西班牙语中实现了竞争性表现.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 从非结构化医疗文本中提取药物提及是很困难的,没有特定语言的注释数据.
- 多语言机器学习模型为跨语言的知识转移提供了解决方案.
- 当目标语言缺乏培训数据时,利用现有的英语资源是关键.
研究的目的:
- 调查用于药物名称提取的多语言变压器模型的有效性.
- 评估德语,英语,法语和西班牙语的多语言和跨语言表现.
- 提供关于跨语言知识转移在医学文本分析中的好处的经验证据.
主要方法:
- 微调一个用于命名实体识别 (NER) 的多语言变压器模型.
- 在多语言和跨语言环境中应用该模型.
- 评估四种欧洲语言发布数据集的性能.
- 进行定性错误分析,以确定预测错误的来源.
主要成果:
- 多语言变压器模型在所有测试语言中在药物名称提取方面取得了竞争性表现.
- 医学知识的跨语言转移证明对德语,英语,法语和西班牙语有效.
- 标注不一致和模糊的实体标签被确定为错误来源.
结论:
- 多语言变压器模型是医学领域跨语言药物名称提取的可行方法.
- 在欧洲语言之间,医疗NLP能力的有效转移是可能的.
- 提高注释质量和指导方针对于提高模型准确性至关重要.
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