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.

翻译：方面级情感分析旨在预测句子中方面术语的情感极性。利用图神经网络从句法依存分析中捕获结构模式，已被证实是提升方面级情感分析的有效方法。在现有工作中，依存树的拓扑结构或基于依存关系的注意力系数往往被松散地视为方面词与观点词之间的边，这可能导致句法利用不充分且模糊不清。针对这些问题，我们提出了一种新的强化依赖图卷积网络，该网络从距离和类型两个维度改进了依存关系的重要性计算。首先，我们提出了依存树最小距离的重要性计算准则。在该准则下，我们设计了一种距离重要性函数，利用强化学习进行权重分布搜索和相异性控制。由于依存类型通常不具备如树距离那样的显式句法结构，我们采用全局注意力与掩码机制来设计类型重要性函数。最后，我们合并这些权重，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及重要性函数的有效性。在所有验证中，RDGCN均优于当前最先进的基于图神经网络的基线模型。