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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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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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DiffFSRE:扩散增强型原型网络,用于少数拍摄关系提取.

Yang Chen1, Bowen Shi2

  • 1State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.

Entropy (Basel, Switzerland)
|May 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新型的扩散模型,用于增强数据以进行少数拍摄关系提取,通过减少过拟合和增强对新关系的概括来改进原型网络,特别是在低资源环境中.

关键词:
扩散模型的扩散模型.进入的过程中,几次射击的学习学习这是原型网络的原型.关系提取关系提取

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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:

  • 用于关系提取的监督学习严重依赖于大型数据集.
  • 少数拍摄关系提取通过从有限的样本中学习来解决数据稀缺问题.
  • 原型网络在少数拍摄关系提取中很常见,但容易过度拟合.

研究的目的:

  • 为了减轻原型网络中的过度拟合,用于几次拍摄的关系提取.
  • 增强对未见关系类的概括能力.
  • 利用扩散模型在这个领域进行数据增强.

主要方法:

  • 开发了一个可控制的条件关系生成扩散模型,例如表示生成.
  • 提出了使用扩散模型的伪样本增强型原型网络.
  • 引入了一个伪样本意识的注意力机制,具有交叉损失.

主要成果:

  • 拟议的扩散模型增强的原型网络显著优于现有方法.
  • 特别是在低资源,一次性学习环境中观察到卓越的表现.
  • 废弃性研究证实了每个模型组件的有效性和必要性.

结论:

  • 基于扩散模型的数据增强有效地解决了几次拍摄关系提取中的过拟合问题.
  • 该框架增强了原型表示和概括到未见的关系.
  • 这项工作开创了使用扩散模型用于数据增强的几次拍摄关系提取.