Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets.

翻译：方面级情感分析（ABSA）是情感分析中的细粒度任务。为了更好地理解复杂长句并获取准确的方面特定信息，该任务通常需要语言知识和常识知识。然而，当前大多数方法采用复杂且低效的方式融入外部知识（例如直接搜索图节点）。此外，外部知识与语言信息之间的互补性尚未得到充分研究。为此，我们提出知识图谱增强网络KGAN，旨在有效融合外部知识与显式句法和上下文信息。具体而言，KGAN从多个不同视角（即基于上下文、句法和知识）捕获情感特征表示。首先，KGAN并行学习上下文表示和句法表示，以充分提取语义特征。然后，将知识图谱集成到嵌入空间中，并基于此通过注意力机制进一步获取方面特定的知识表示。最后，我们提出层次融合模块，以局部到全局的方式补充这些多视角表示。在五个主流ABSA基准上的大量实验证明了KGAN的有效性和鲁棒性。值得注意的是，在预训练模型RoBERTa的辅助下，KGAN在所有数据集上取得了新的最优性能纪录。