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通过大型语言模型创建注释数据集,用于非英语医学NLP

Johann Frei¹, Frank Kramer¹

¹IT-Infrastructure for Translational Medical Research, University of Augsburg Alter Postweg 101, 86159 Augsburg, Germany.

Journal of biomedical informatics

|August 25, 2023

概括

利用大型语言模型 (LLM) 有助于获取用于自然语言处理 (NLP) 的语义注释数据集. 这种方法可以为特定任务培训高效,较小的模型,即使在非英语医疗环境中.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
机器学习机器学习
医疗信息学医疗信息学

背景情况:

在NLP的监督培训需要对文本数据集进行艰苦的语义注释.
特定领域的NLP任务,特别是在非英语医学环境中,由于缺乏量身定制的数据集和预先训练的模型,面临着挑战.

研究的目的:

提出和演示一种方法,以使用预先训练的大型语言模型来获取大规模的语义注释数据集.
通过利用LLM生成的数据,为特定的NLP任务培训更小,更有效的模型.

主要方法:

利用预训练的大型语言模型进行自动化训练数据采集.
开发了一套定制的数据集,用于训练德国文本的医疗命名实体识别 (NER) 模型.
确保拟议的方法原则上独立于语言.

主要成果:

成功创建了德国医学NER的自定义数据集.
使用获得的数据集训练了一种高效的医疗NER模型 (GPTNERMED).
证明了在资源稀缺的语言领域使用LLM用于数据采集的有效性.

结论:

预先培训的LLM提供了一个可行的解决方案,以克服监督NLP的数据稀缺性,特别是针对专业领域和语言.
提出的方法有助于开发高效的,具体任务的NLP模型.
数据集和模型是公开可用的,以支持进一步的研究.

关键词:

数据增强数据增强提取信息提取信息知识的蒸知识的蒸.药物检测药物检测药物检测命名实体认可命名实体认可自然语言处理自然语言处理.

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