多模态情绪分析的层次性文本导向精细化网络
1School of Mathematical Sciences, Capital Normal University, Beijing 100048, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了多式情绪分析 (MSA) 的等级文本导向精细化网络 (HTRN). 该HTRN有效地调整非文本特征并减少冗余性,在基准数据集上实现最先进的结果.
科学领域:
- 人工智能
- 计算机科学
- 自然语言处理
背景情况:
- 多模态情绪分析 (MSA) 整合了文字,音频和视频以提高理解.
- 现有的方法在调整非文本特征和减轻信息冗余方面扎.
研究的目的:
- 为改进MSA提出一个新的等级文本导向改进网络 (HTRN).
- 增强跨模式交互并抑制多模式数据中的无关信号.
主要方法:
- 在HTRN框架中,使用层次化的文本表示方式来完善和调整非文本形式.
- 混合插入融合 (SIF) 破坏了一般表示的局部相关性.
- 文本导向对齐层 (TAL) 使用文本语义来通过可学习的门因素引导视听精细化.
主要成果:
- HTRN实现了最先进的精度:86.3% (CMU-MOSI),86.7% (CMU-MOSEI) 和80.3% (CH-SIMS).
- 与现有方法相比,性能改善在0.8-3.45%之间.
- 废除研究证实SIF和TAL有助于1. 9 - 2. 1%的性能增长.
结论:
- 这一HTRN框架有效地解决了多式联运和冗余性方面的挑战.
- 提出的方法显著提升了多式联络情绪分析的性能.
- 该网络为多式联络学习建立了一个强大的框架.
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