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情境意识的对比表示学习为零射击生物医学文本分类.

Ratri Mukherjee1, Kishlay Jha1

  • 1Department of Electrical and Computer Engineering University of Iowa, IA, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
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PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的零射击学习 (ZSL) 方法用于生物医学文本分类,减少了对广泛标记数据的需求. 该方法使用上下文意识的对比学习来更准确地使用标签名称进行分类.

关键词:
生物医学多标签文本分类 文本分类相反的学习学习学习.零射击学习的学习

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 生物医学文本分类通常需要广泛的人类注释数据,这是昂贵和耗时的.
  • 现有的方法经常因数据稀缺和高注释费用而扎.

研究的目的:

  • 为生物医学文本分类开发零射击学习 (ZSL) 方法,消除对标记训练数据的需求.
  • 为培训和推理利用标签表面名称,使过程更有效率.

主要方法:

  • 提出了一种针对生物医学领域的ZSL量身定制的新型情境感知对比学习技术.
  • 开发了一种方法,通过利用生物医学文本中固有的上下文来生成语义上有意义的对比对,与随机细分策略不同.

主要成果:

  • 提出的情境感知对比学习方法显著提高了零射击生物医学文本分类准确性.
  • 在大型生物医疗体上的实验证明了新方法的有效性和优越性.

结论:

  • 零射击学习,特别是具有上下文意识的对比学习,为生物医学文本分类挑战提供了一个有希望的解决方案.
  • 这种方法减少了对昂贵的标记数据集的依赖,使生物医学研究的应用更广泛.