建模结构化依赖树与图形卷积网络,以进行面向级情感分类
Qin Zhao1,2, Fuli Yang1, Dongdong An1
1Department of Computer Science and Technology, Shanghai Normal University, Shanghai 200234, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究介绍了一个基于结构依赖树的图形卷积网络 (SDTGCN),以改进基于方面的情绪分析. 该模型精确地捕捉了上下文关系,显著提高了情绪分类准确性和F1分数.
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:
- 基于方面的情感分析需要精细地预测句子中的特定方面的情感极性.
- 当前的图形神经网络模型经常使用依赖树,但会受到杂的节点的影响,并且无法捕捉到关键的间接关系.
- 现有的方法忽略了节点之间的连接,没有直接依赖边缘,这显著影响情绪极性.
研究的目的:
- 提出一个新的基于结构依赖树的图形卷积网络 (SDTGCN) 模型.
- 通过改进对上下文关系和情感依赖性的捕捉来解决现有模型的局限性.
- 为了提高情感分析中方面表示的精度.
主要方法:
- 构建一个结构化的语法依赖图,包括位置信息,情感常识知识,部分语音标签和依赖距离.
- 分配任意边缘权重以增强侧面节点和关键词之间的连接,同时削弱不相关的链接.
- 使用部分语音标签和依赖距离来识别没有直接依赖的节点之间的关系.
- 根据重要性对节点信息进行聚合,以获得精确的方面表示.
主要成果:
- 在五个公开可用的数据集上,SDTGCN模型在最先进的方法上表现出优越性.
- 在大多数数据集的准确性和F1得分上观察到显著的改进,增幅高达1.17.
- 该模型通过更好地表达情绪依赖,有效地提高了情绪分类性能.
结论:
- 拟议的SDTGCN模型为基于方面的情绪分析提供了显著的进步.
- 结合结构化的语法信息和常识知识,可以提高模型捕捉微妙情绪的能力.
- 增强的性能验证了SDTGCN方法对精确情绪分类的有效性.
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