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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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通过语义指导式学习改进少数拍摄关系提取.

Hui Wu1, Yuting He2, Yidong Chen3

  • 1Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, 361005, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, Xiamen, 361005, China.

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PubMed
概括
此摘要是机器生成的。

语义导向学习 (SemGL) 通过增强实例和原型表示来改进几次拍摄的关系提取. 这种方法有效地利用关系信息,提高挑战性域调整任务的性能.

关键词:
相反的学习学习.几次拍摄的关系提取.网络原型网络原型关系图的学习关系图.关系信息 关系信息语义指导的学习是指导语义的学习.

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 少数拍摄的关系提取 (少数拍摄的RE) 使用有限的数据识别实体之间的关系.
  • 现有的原型网络方法增强了表示或使用对比学习,但与异常值和类混作斗争.

研究的目的:

  • 提出语义导向学习 (SemGL) 以提高少量射击的RE性能.
  • 通过有效利用关系信息来增强实例和原型表示.

主要方法:

  • SemGL使用一个提示符编码器,通过大型语言模型进行语义表示增强.
  • 关系图学习集群使用概念原型的实例.
  • 实例级和原型级的对比学习被用来改进特征歧视.

主要成果:

  • 在短时间内,SEMGL 已经证明了其在短时间内可再生能源的有效性和效率.
  • 该方法显示出有希望的结果,特别是在域调整挑战方面.
  • 在两个公共数据集上的实验验证证证了SemGL的性能.

结论:

  • 通过整合语义指导,SemGL提供了一种新的方法来提取几次拍摄的关系.
  • 该方法有效地解决了现有技术的局限性,提高了准确性和稳定性.
  • 在低资源场景中,SemGL显示了推进关系提取的潜力.