多式联络情绪分析的多式联络情绪分析的局部和全球演示的层次图对比学习
Jun Du1, Jianhang Jin2, Jian Zhuang1
1School of Physics and Electronics, Shandong Normal University, Shandong, China.
Scientific reports
|March 4, 2024
概括
这项研究引入了一种新的层次图对比学习框架,用于多式联络情绪分析 (MSA). 它有效地捕捉了本地和全球的表征在发言中,以改善情绪提取.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 多模态情绪分析 (MSA) 使用声学,视觉和文本线索来确定发言情绪.
- 现有的MSA方法往往忽视数据集中的全球共发生模式,主要关注语境内表示.
- 这种局限性阻碍了从各种数据源中获得的情绪的全面理解.
研究的目的:
- 为多模态情绪分析 (MSA) 提出一个新的层次图对比学习框架.
- 在单个发言中探索本地和全球的表征,以增强情感提取.
- 调查地方和全球情绪特征之间的复杂关系.
主要方法:
- 提取每个模式的离散嵌入式表示,结合全球共发生特征.
- 每个发言构建两个不同的图形:一个地方级图形和一个全球级图形.
- 应用双图对比学习策略与图增强和跨层次的比较学习.
主要成果:
- 拟议的框架通过模拟本地和全球图形结构,有效地学习特定水平的情绪影响.
- 双对比学习策略增强了基于图形增强的各种表示的探索.
- 跨层次的比较学习促进了局部和全球代表的整合,用于复杂的关系建模.
结论:
- 层次图对比学习框架在多模式情绪分析中提供了显著的进步.
- 通过考虑本地和全球特征,该模型可以更细致地了解情绪.
- 这种方法提供了一种强大的方法来提取情绪,通过利用多模式数据中的复杂关系来提取情绪.
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