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科学报告多关系双重注意力图形变换器用于细粒度情绪分析
Anusha P Anilkumar1, Soo-Kyun Kim2, Yeo-Chan Yoon3
1Department of Artificial Intelligence, Jeju National University, 63243, Jeju City, Republic of Korea.
Scientific reports
|February 4, 2026
概括
我们介绍了多关系双重注意力图形转换器 (MRDAGT),用于基于方面的情绪分析. 这种模型通过整合各种语言关系来提高情绪预测的准确性和可解释性.
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
- 计算语言学 计算语言学
背景情况:
- 基于方面的情感分析 (ABSA) 需要模拟复杂的语言依赖关系.
- 现有的基于图形的方法与多关系数据和可解释性作斗争.
- 缺乏有效的注意力机制阻碍了精确的情绪极性识别.
研究的目的:
- 提出一个新的框架,多关系双重注意力图形变压器 (MRDAGT),用于增强ABSA.
- 在统一的图形架构中有效地整合语法,语义和话语级别的关系.
- 提高情绪分析模型的解释性和性能.
主要方法:
- 开发了一个新的图形架构,将多种关系类型统一起来.
- 实施了一种双重注意力机制,以实现平衡的本地和面向集中交互.
- 整合了注意力规范化 (基于的惩罚,L1稀疏) 为了可解释性.
主要成果:
- 在多个ABSA数据集中,MRDAGT建立了新的最先进的基准.
- 在情绪极性识别方面取得了实质性的性能改进.
- 证明了透明和基于语言的预测流程.
结论:
- 拟议的MRDAGT框架显著促进了ABSA的发展.
- 双重注意力和规范化技术提高了性能和可解释性.
- 对于现实世界的情绪分析应用,MRDAGT提供了一个强大的解决方案.
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