使用域内适应的BERT模型和分类层识别和规范化多语言症状实体
Fernando Gallego1,2, Francisco J Veredas1,2
1Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Blvr. Louis Pasteur, 35, Puerto de la Torre, Málaga 29071, Spain.
Database : the journal of biological databases and curation
|August 28, 2024
概括
这项研究通过开发用于检测和链接临床实体的新方法来推进低资源语言的临床自然语言处理. 这种新方法在多语言临床文本分析中设定了最先进的基准.
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:
- 临床自然语言处理 (NLP) 具有挑战性,因为注释数据有限,特别是对于资源较低的语言.
- 在多语言医学文本中开发强大的方法来识别和规范临床实体 (症状,迹象,发现) 是至关重要的.
研究的目的:
- 通过对临床实体的检测和规范化做出贡献,解决临床NLP对低资源语言的挑战.
- 在SympTEMIST共享任务中开发和评估命名实体识别 (NER) 和命名实体链接 (NEL) 的模型.
主要方法:
- 对于NER (子任务1),在西班牙语临床文本中使用了基于BERT的模型组合,该模型组合在瘤学体内进行了预训练.
- 对于NEL (子任务2和3),开发了一种使用对比学习训练双编码器 (类似SapBERT的模型) 的分类策略.
- 通过使用机器翻译工具将西班牙语知识库翻译成其他语言来实现多语言能力.
主要成果:
- 提出的方法在SympTEMIST挑战的三个子任务中取得了最先进的结果.
- 小任务1 (西班牙语NER) 的结果是精度=0.804,F1得分=0.748,回忆=0.699.
- 副任务2 (西班牙语NEL) 使用WNT-softmax层在top-1准确度上显示了高达5.5%的性能增长.
- 副任务3 (多语种NEL) 显示了显著的改进,多语种双编码器在大多数语言中表现优于其他模型,在top-1准确度中获得了13% (葡萄牙语) 和13.26% (瑞典语) 的收益.
结论:
- 开发的方法显著提升了临床实体检测和多语言临床文本规范化的最新技术.
- 该方法证明了基于BERT的模型和在低资源的临床NLP场景中为命名实体链接的对比学习的有效性.
- 这项工作为未来跨语言临床信息提取研究提供了坚实的基础,并支持为各种语言环境开发NLP工具.
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