arXiv 2311.044672023-11-08cs.CLcs.AI

RDGCN：面向方面级情感分析的强化依赖图卷积网络

RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis

Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；University of Science and Technology of China（中国科学技术大学）；Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）

Xusheng Zhao, Hao Peng, Qiong Dai, Xu Bai, Huailiang Peng, Yanbing Liu, Qinglang Guo, Philip S. Yu

备注：: The 17th ACM International Conference on Web Search and Data Mining

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AI总结：

针对ABSA中句法依存利用不充分的问题，提出RDGCN模型，从距离和类型维度优化依存重要性计算，在三个数据集上优于现有GNN基线。

摘要：

方面级情感分析（ABSA）致力于预测句子中方面词的情感极性。利用图神经网络从句法依存解析中捕捉结构模式已被证实是提升ABSA性能的有效途径。在大多数研究中，依存树的拓扑结构或基于依存的注意力系数常被宽泛地视为方面与观点之间的边，这会导致句法信息利用不充分且模糊。为解决这些问题，我们提出了一种新型强化依赖图卷积网络（RDGCN），从距离和类型两个维度改进依存关系的重要性计算。首先，我们提出了一种依存树最小距离的重要性计算准则。在该准则下，我们设计了距离重要性函数，借助强化学习进行权重分布搜索和差异性控制。由于依存类型通常不像树距离那样具有明确的句法特征，我们使用全局注意力和掩码机制设计类型重要性函数。最后，我们融合这些权重并实现特征聚合与分类。在三个主流数据集上的综合实验验证了该准则和重要性函数的有效性。RDGCN在所有验证中均优于当前最优的基于GNN的基线模型。

英文摘要：

Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences. Employing graph neural networks to capture structural patterns from syntactic dependency parsing has been confirmed as an effective approach for boosting ABSA. In most works, the topology of dependency trees or dependency-based attention coefficients is often loosely regarded as edges between aspects and opinions, which can result in insufficient and ambiguous syntactic utilization. To address these problems, we propose a new reinforced dependency graph convolutional network (RDGCN) that improves the importance calculation of dependencies in both distance and type views. Initially, we propose an importance calculation criterion for the minimum distances over dependency trees. Under the criterion, we design a distance-importance function that leverages reinforcement learning for weight distribution search and dissimilarity control. Since dependency types often do not have explicit syntax like tree distances, we use global attention and mask mechanisms to design type-importance functions. Finally, we merge these weights and implement feature aggregation and classification. Comprehensive experiments on three popular datasets demonstrate the effectiveness of the criterion and importance functions. RDGCN outperforms state-of-the-art GNN-based baselines in all validations.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。