GSMNet: Global Semantic Memory Network for Aspect-Level Sentiment Classification
Zhiyue Liu, Jiahai Wang, Xin Du, Yanghui Rao, Xiaojun Quan
Abstract
Aspect-level sentiment classification determines the sentiment polarity of a targeted aspect. To solve this task, attention-based neural networks are typically adopted to explore the interaction between the aspect and its context in a single sentence. However, such approaches ignore the rich semantic information that can be obtained from other sentences. This article shows that the contexts of aspects with similar meanings should be considered global semantic information that can be incorporated as domain knowledge. Then, a novel global semantic memory network (GSMNet) is proposed to share the global semantic information of various aspects and generate a domain-specific representation. With the help of domain knowledge, crucial words can be focused on more precisely. Moreover, instead of employing the concatenating operation for vectors before classification, GSMNet adopts a fine-grained information fusion layer to capture the importance of representations for efficiently extracting the valid parts of each dimension. The experimental results demonstrate the effectiveness of our model.