双语法意识图注意力网络,提示基于方面情绪分析
Ao Feng1, Tao Liu2, Xiaojie Li1
1School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
Scientific reports
|October 9, 2024
概括
本研究介绍了一种新的双语法感知图表注意力网络,用于基于方面的情绪分析 (ABSA) 的提示 (DSGP) 模型. DSGP模型增强了预先训练的语言模型和语法信息提取,以提高情绪分析的准确性.
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) 是复杂的,因为在句子的多个方面和两极性.
- 预先训练的语言模型 (例如BERT) 和图形神经网络 (GNN) 是有前途的,但也有局限性.
- 现有的方法与不规则的语法作斗争,并未充分利用先进的预训练模型.
研究的目的:
- 为ABSA提出了一个新的双语法意识的图形注意力网络与Prompt (DSGP) 模型.
- 解决处理复杂句子结构和利用预训练模型的现有方法的局限性.
- 提高基于方面的情绪分析的准确性和稳定性.
主要方法:
- DSGP模型使用提示模板来最大限度地发挥预训练模型的潜力.
- 它使用模板中的掩盖向量输出作为补充的方面特征.
- 它将依赖和构成树与图表注意力网络集成在一起,用于全面的语法信息提取.
主要成果:
- 拟议的DSGP模型证明了四个公共数据集的竞争性表现.
- 来自双树的提示输出和语法信息的融合增强了特征表示.
- 该模型有效地捕捉了细粒度的单词相关性和高级句子结构.
结论:
- DSGP模型在基于方面的情绪分析中提供了显著的进步.
- 利用双重语法树和基于提示符的预训练模型可以提高性能.
- 该方法在情感分析中处理复杂的语言现象方面表现出前途.
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