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Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
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PromptSED:一个不断发展的主题增强的提示框架,用于增量检测社会事件.

Xiaoyan Yu1, Jiaqian Ren2, Lei Jiang3

  • 1School of Computer Science and Technology, Beijing Institute of Technology, 100081, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|July 11, 2025
PubMed
概括

PromptSED通过使用不断演变的主题增强的快速学习框架来增强社会事件检测 (SED). 该方法动态跟踪主题并将其注入预训练语言模型 (PLM) 中,以改进增量事件识别.

关键词:
预先训练有素的语言模型基于提示符的微调.社会事件检测,社会事件检测.

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 社交媒体分析

背景情况:

  • 基于变压器的预训练语言模型 (PLM) 显示了社会事件检测 (SED) 的前景.
  • SED面临的挑战是短,杂的社交媒体文本和不断出现的事件.
  • 现有的方法与社会数据流的动态性质作斗争.

研究的目的:

  • 引入PromptSED,一个不断发展的主题增强的快速学习框架,用于增量社会事件检测.
  • 为解决PLM在处理杂,不断变化的社会数据方面的局限性,为SED.
  • 允许在社交流中进行无培训,动态的主题跟踪和事件检测.

主要方法:

  • PromptSED采用选择性主题注入,将不断变化的主题转换为PLM的提示.
  • 耐噪音优化策略可以提高弹性和事件差异化.
  • 免费培训主题跟踪可以促进增量SED,而无需额外的标签.

主要成果:

  • 在两个公共数据集上,PromptSED在社会事件检测方面取得了显著的性能改进.
  • 组件分析验证了选择性主题注射,耐噪音优化和无训练主题跟踪的有效性.
  • 进行比较分析,评估了仅用解码器的大型语言模型对SED.的适用性.

结论:

  • PromptSED为增量社会事件检测提供了一个有效的框架,克服了社会数据中的挑战.
  • 提出的方法提高了PLM的适应性和稳定性,用于动态事件识别.
  • 该框架展示了强大的性能和现实世界的社交媒体事件监控的潜力.