Dependency Parsing and Attention Network for Aspect-Level Sentiment Classification

摘要：

　　Aspect-level sentiment classification aims to determine the sentiment polarity of the sentence towards the aspect.The key element of this task is to characterize the relationship between the aspect and the contexts.Some recent attention-based neural network methods regard the aspect as the attention calculation goal,so they can learn the association between aspect and contexts directly.However,the above attention model simply uses the word embedding to represent the aspect,it fails to make a further improvement on the performance of aspect sentiment classification.To solve this problem,this paper proposes a dependency subtree attention network(DSAN)model.The DSAN model firstly extracts the dependency subtree that contains the descriptive information of the aspect based on the dependency tree of the sentence,and then utilizes a bidirectional GRU network to generate an accurate aspect representation,and uses the dot-product attention function for the dependency subtree aspect representation,which finally yields the appropriate attention weights.The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the DSAN model.

关键词： Aspect-level sentiment classification Attention network Dependency tree

作者: Zhifan Ouyang Jindian Su

作者单位: School of Computer Science and Engineering,South China University of Technology,Guangzhou,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 391-403

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）