Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
This work addresses aspect-based sentiment classification for natural language processing applications, but it is incremental as it builds on existing GCN and dependency tree methods.
The paper tackled the problem of aspect-based sentiment classification by addressing the lack of syntactic constraints and long-range dependencies in existing models, proposing an aspect-specific Graph Convolutional Network (GCN) over dependency trees. The result showed comparable effectiveness to state-of-the-art models on three benchmarking collections, with the model properly capturing syntactical information and long-range word dependencies.
Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.