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数据增强为少数镜头生物医学NER使用ChatGPT.

Wenxuan Mu1, Di Zhao1, Jiana Meng1

  • 1Dalian Minzu University, Dalian, 116650, China.

Artificial intelligence in medicine
|December 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的数据增强方法,用于使用ChatGPT和快速学习进行生物医学命名实体识别 (NER). 该方法在低数据场景中提高了模型性能,在少数镜头设置中实现了高精度.

关键词:
生物医学命名实体的识别.聊天GPT 聊天GPT 聊天数据增强数据增强有几次射击学习学习.自然语言处理自然语言处理.快速学习 快速学习可以分离的卷曲.转移学习转移学习

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科学领域:

  • 生物医学自然语言处理
  • 机器学习 机器学习

背景情况:

  • 数据稀缺是生物医学命名实体识别 (NER) 中的一个重大挑战.
  • 少数拍摄的学习场景需要有效的数据增强 (DA) 来改善模型概括性并减少过度拟合.
  • 现有的DA方法可能无法充分解决生物医学文本的复杂性.

研究的目的:

  • 为生物医学NER任务提出一种新的DA方法.
  • 利用像ChatGPT这样的大型语言模型 (LLM) 来生成高质量的数据.
  • 为了提高NER模型在低数据和少数镜头设置中的性能.

主要方法:

  • 利用ChatGPT和快速学习来提取NER的高质量数据.
  • 员工转移学习和对实体识别的有效解码策略.
  • 在四个公共生物医学数据集上进行实验:BC5CDR,NCBI,BioNLP11EPI和BioNLP13GE.

主要成果:

  • 拟议的DA方法在极其有限的数据场景中显示出强大的稳定性和实体识别能力.
  • 在四个数据集中,F1平均得分为72.96% (5次射击),75.05% (20次射击) 和77.42% (50次射击).
  • 在少数NER任务中显示了模型概括能力的显著改进.

结论:

  • 新的DA方法有效地解决了生物医学NER中的数据稀缺问题.
  • 聊天GPT和快速学习为生成高质量的培训数据提供了强大的方法.
  • 该方法显示了改善NER模型性能在具有挑战性的,低资源的生物医学领域的前景.