单词距离辅助双图形卷积网络,用于准确和快速的方面级情绪分析
Jiajia Jiao1, Haijie Wang1, Ruirui Shen1
1College of Information Engineering, Shanghai Maritime University, Shanghai, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
这项研究引入了一种双图形卷积网络 (GCN),通过结合单词距离来改进面层情绪分析. 这种新的方法提高了准确性,并大大加快了对情绪分类任务的训练.
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:
- 面向级情绪分析为特定主题提供了细粒度的情绪分类.
- 图形卷积网络 (GCN) 在情感分析中很受欢迎,用于捕捉词汇相关性.
- 现有的GCN方法经常忽视单词距离,导致错误分类.
研究的目的:
- 提出一种新的双 GCN 结构,将单词距离,语法信息和情感知识整合在一起.
- 解决因在GCN中忽视单词距离而导致的交叉错误分类问题.
- 为了提高层面情绪分析的准确性和效率.
主要方法:
- 开发了一种双重GCN结构,将单词距离纳入其中.
- 利用单词距离来增强语法依赖树.
- 使用单词距离构建了一个新的图形,用语义知识.
- 将两个单词的距离辅助图表入单独的GCN进行分类.
主要成果:
- 与最先进的方法相比,实现了更高的分类准确性.
- 演示了显著的训练加速,高达1.81x.
- 在中英两种数据集上验证了方法,包括MOOC评论和杜班书评.
结论:
- 拟议的双GCN结构有效地利用单词距离,语法和语义信息,以改进层面情绪分析.
- 该方法为准确和高效的情绪分类提供了一个有希望的解决方案.
- 该方法在各种数据集中显示出卓越的性能.
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