基于变压器的多标签NER使用多种语言的维基百科体
Yelyzaveta Ahapova1, Johann Frei1, Frank Kramer1
1IT-Infrastructure for Translational Medical Research, University of Augsburg, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
本研究介绍了一种无监督的方法,以多种语言创建医疗文本数据集,用于命名实体识别 (NER). 这种方法提高了德国药物识别,特别是有限的数据.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 医疗文本的手动数据标签是昂贵的,并引发隐私问题,导致缺乏非英语医学注释.
- 现有的方法通常需要大量的标记数据,这对于许多语言来说很少.
- 基于本体学的语料库构建为数据稀缺提供了一个潜在的解决方案.
研究的目的:
- 评估一种无监督的方法,用于从维基百科创建由本体学注释的corpora,用于医学命名实体识别 (NER).
- 评估这种方法在英语,德语,西班牙语和法语的有效性.
- 改善药物和诊断实体在低资源医疗文本场景的识别.
主要方法:
- 一种无监督的方法被用来构建来自维基百科 (Wikidata) 的本体学注释的 corpora.
- 该方法用于生成英语,德语,西班牙语和法语的多标签 corpora.
- 生成的体被用来训练和评估用于药物和诊断实体识别的模型.
主要成果:
- 无监督方法在德国药物实体检测方面取得了显著的改进,特别是在稀疏注释条件下.
- 在实体识别任务中,在英语,德语,西班牙语和法语中观察到一致的性能.
- 与基线方法相比,生成的多标签大体在提高NER性能方面表现出有效性.
结论:
- 无监督的基于本体学的语料库构建是解决非英语语言医疗注释稀缺问题的可行策略.
- 这种方法为医疗NER的手动数据标签提供了一个具有成本效益和隐私保护的替代方案.
- 这种方法有望改善跨语言医学信息提取和分析.
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