  Aspect-level sentiment classification (ASC) aims to predict the fine-grained
sentiment polarity towards a given aspect mentioned in a review. Despite recent
advances in ASC, enabling machines to preciously infer aspect sentiments is
still challenging. This paper tackles two challenges in ASC: (1) due to lack of
aspect knowledge, aspect representation derived in prior works is inadequate to
represent aspect's exact meaning and property information; (2) prior works only
capture either local syntactic information or global relational information,
thus missing either one of them leads to insufficient syntactic information. To
tackle these challenges, we propose a novel ASC model which not only end-to-end
embeds and leverages aspect knowledge but also marries the two kinds of
syntactic information and lets them compensate for each other. Our model
includes three key components: (1) a knowledge-aware gated recurrent memory
network recurrently integrates dynamically summarized aspect knowledge; (2) a
dual syntax graph network combines both kinds of syntactic information to
comprehensively capture sufficient syntactic information; (3) a knowledge
integrating gate re-enhances the final representation with further needed
aspect knowledge; (4) an aspect-to-context attention mechanism aggregates the
aspect-related semantics from all hidden states into the final representation.
Experimental results on several benchmark datasets demonstrate the
effectiveness of our model, which overpass previous state-of-the-art models by
large margins in terms of both Accuracy and Macro-F1.
