一个动态图形结构框架,用于隐式情绪识别,基于互补的语义和结构信息.
Yuxia Zhao1,2,3, Mahpirat Mamat1,4, Alimjan Aysa1,4
1School of Computer Science and Technology, Xinjiang University, Ürümqi, 830046, Xinjiang, China.
Scientific reports
|July 17, 2024
概括
本研究引入了一种新的动态图框架 (SIF),通过整合语义和结构文本信息来改善隐性情绪识别. SIF动态学习图形结构,在多个数据集上表现优于现有的方法.
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
- 数据挖掘 数据挖掘
背景情况:
- 隐含的情感识别是具有挑战性的,因为没有明确的情感词.
- 目前用于NLP的图形神经网络 (GNN) 方法在图形结构构建和依赖手工专业知识方面存在局限性.
- 现有的静态图形结构往往过于简单,容易出现错误,阻碍了语义理解.
研究的目的:
- 提出一个动态图形结构框架 (SIF),解决隐式情绪识别的局限性.
- 通过整合互补的语义和结构信息来增强特征表示.
- 动态学习图形结构,减少对手工输入的依赖和纠正错误.
主要方法:
- 开发了一个动态图形结构框架 (SIF),利用语义和结构信息的互补性.
- 构建了独立的结构和语义信息图表,以丰富特征表示.
- 实现了初始静态图形结构的动态学习,以减轻噪声和错误.
主要成果:
- 在三个公共数据集上,SIF显著优于主流自然语言处理 (NLP) 方法.
- 实现了高准确率:在Puns of day上达到95.73%,在SemEval-2021任务7上达到85.37%,在Reddit上达到65.36%.
- 证明了集成动态图形结构用于隐式情绪分析的有效性.
结论:
- 拟议的SIF框架为隐式情绪识别提供了一个强大的解决方案.
- 动态图表学习有效地捕捉复杂的文本语义,并提高模型性能.
- 在情绪分析中,SIF显示了对现实世界应用的巨大潜力.
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