CKG:通过使用ChatGPT和知识增强的封闭注意力图卷积网络进行文本增强来改进ABSA
Yapeng Gao1, Lin Zhang1, Yangshuyi Xu1
1College of Information Engineering, Shanghai Maritime University, Shanghai, China.
PloS one
|June 27, 2024
概括
这项研究通过使用ChatGPT进行数据增强和改进的封闭注意力机制来增强方面级情绪分析 (ABSA). 新模型有效地整合了功能,在情绪极性预测中表现优于现有方法.
科学领域:
- 自然语言处理自然语言处理.
- 神经机器人技术的发展
背景情况:
- 面层情绪分析 (ABSA) 对于理解文本中微妙的情绪至关重要.
- 有限的培训数据和功能集成问题阻碍了当前的ABSA模型.
- 以前使用图形卷积的方法面临着梯度爆炸问题.
研究的目的:
- 为了研究ChatGPT的方面级文本增强.
- 为图形卷积网络引入一个改进的封闭注意力机制.
- 为了增强ABSA的语义和语法特征的整合.
主要方法:
- 利用ChatGPT进行面层文本增强.
- 开发一种改进的封闭注意力机制,以减轻图形卷积网络中的梯度爆炸.
- 通过情感知识库来丰富依赖图的特征,并使用交叉融合来实现特征集成.
主要成果:
- 与基线模型相比,拟议的模型表现出优越的性能.
- 实现了语义和语法特征的有效整合.
- 改进的封闭注意力机制成功解决了梯度爆炸问题.
结论:
- 该研究提出了一种使用ChatGPT和增强图形卷积网络的ABSA新方法.
- 调查结果强调了拟议方法在克服数据稀缺和特征集成挑战方面的有效性.
- 该模型显示了情绪极性预测准确度的显著改善.
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