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GERNERMED++:通过转移学习,翻译和词汇对齐,在德国医学NLP中进行语义注释

Johann Frei¹, Ludwig Frei-Stuber², Frank Kramer¹

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

Journal of biomedical informatics

|October 14, 2023

概括

我们开发了GERNERMED++,这是德国医疗名称实体识别 (NER) 的新模型. 这种开源工具通过超越现有模型来增强医学NLP研究.

科学领域:

计算语言学计算语言学
医疗信息学医学信息学
人工智能的人工智能是人工智能.

背景情况:

德国医学命名实体识别 (NER) 开源模型的有限可用性.
需要提高从德语文本中提取医疗实体的性能.

研究的目的:

介绍GERNERMED++,这是德国医疗NER的先进统计模型.
提供公开可用的基线模型,使德国研究界受益.

主要方法:

在预先训练的深度语言模型上利用转移学习.
集成的文字对齐和神经机器翻译技术.
在多个数据集上训练和评估GERNERMED++模型.

主要成果:

在实体识别方面,GERNERMED++显著超过了先前存在的基准模型.
证明了结合多种先进技术的有效性.
在德国医学文本的命名实体识别中取得了强的表现.

结论:

GERNERMED++为德国医疗NER提供了一个有价值,高性能,开源的解决方案.
这款车型是之前的GERNERMED车型的精致继任者.
模型和代码的公开可用性促进了医疗NLP的进一步研究和开发.

关键词:

人工智能的人工智能是人工智能.德国的NLP.医疗NLP是一种非编程语言.医疗名称实体认可医疗名称实体认可自然语言处理自然语言处理.转移学习转移学习

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