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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Updated: Jun 27, 2025

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通过调整外部知识来进行基于跨度的少数事件事件检测.

Tongtao Ling1, Lei Chen1, Yutao Lai1

  • 1School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China.

Neural networks : the official journal of the International Neural Network Society
|May 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了SpanFSED,这是一种用于Few-shot事件检测 (FSED) 的新方法,通过专注于跨度提取和增强事件分类来提高准确性. 它解决了以前用于识别使用最小数据的新事件类型的方法的局限性.

关键词:
外部知识库 外部知识库几次射击事件检测事件检测.全球边界矩阵全球边界矩阵一个典型的网络原型.

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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:

  • 短拍事件检测 (FSED) 对于在数据有限的新领域识别新型事件类型至关重要.
  • 现有的基于原型网络 (PN) 的方法在学习代币智能标签依赖性和创建准确的原型方面扎.

研究的目的:

  • 提出一种新的基于跨度的FSED模型,SpanFSED,以克服以前基于PN的方法的局限性.
  • 增强对代币智能标签依赖的学习和FSED中的事件原型的准确性.

主要方法:

  • SpanFSED将FSED分解为两个子过程:一个跨度提取器和一个事件分类器.
  • 跨度提取将顺序标签转换为全球边界矩阵,以获得精确的边界信息.
  • 事件分类将事件类型与知识库 (例如,FrameNet) 协调一致,并增强支持集,以触发信息来改进原型设计.

主要成果:

  • 与现有方法相比,SpanFSED表现出优越的性能.
  • 在四个不同的数据集 (ACE2005,ERE,MAVEN,FewEvent) 上进行的实验验证实了该模型的有效性.
  • 提出的方法显著提高了边界信息获取和原型准确性.

结论:

  • 通过解决先前方法的关键局限性,SpanFSED提供了一种更有效的Few-shot事件检测方法.
  • 基于跨度的分解和增强的分类策略有助于提高识别新型事件类型的性能.
  • 该研究提供了可访问的代码和数据,用于FSED的可复制性和进一步研究.