  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.
