基于知识融合的代图形结构学习框架,用于隐式情感识别.
Yuxia Zhao1,2,3, Mahpirat Mamat1,4, Alimjan Aysa1,4
1School of Information Science and Engineering, Xinjiang University, Ürümqi 830046, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种基于知识融合的代图形结构学习框架 (KIG),以改进隐式情绪分析. KIG使用多个来源信息来增强图形结构,在识别微妙的情感表达方面超过现有方法.
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
- 机器学习 机器学习
背景情况:
- 隐含的情感识别对于文本分析至关重要.
- 目前的图形神经网络 (GNN) 方法在有限的结构信息和杂的图形边缘方面扎.
- 这些局限性阻碍了准确捕捉模糊的情感表达.
研究的目的:
- 为了解决目前基于GNN的隐性情绪识别的局限性.
- 开发一个框架,增强结构信息和优化图形拓.
- 提高隐性情绪分析的准确性和稳定性.
主要方法:
- 引入了一个基于知识融合的代图形结构学习框架 (KIG).
- 构建多视图图形结构,使用并发统计数据,等号相似性和语法依赖树.
- 代精制图形结构以更好地适应数据并优化情绪分析.
主要成果:
- 与主流隐性情绪识别方法相比,KIG表现优越.
- 在 Pun of the Day 数据集中实现了高准确度 (89.2%),回忆力 (93.7%) 和 F1 得分 (91.1%).
- 实验结果验证了拟议方法的有效性.
结论:
- 拟议的KIG框架有效地解决了隐性情绪分析现有的GNN方法的局限性.
- 多视图图形构建和代结构学习增强了对微妙情感表达的捕获.
- 基格为隐性情绪识别任务提供了一种卓越的方法.
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