Aspect-Level Sentiment Classification with Conv-Attention Mechanism
Yi Qian; Liu Jie; Zhang Guixuan; Zhang Shuwu
2018
Conference NameInternational Conference on Neural Information Processing (ICONIP)
Conference Date2018.12.13-2018.12.16
Conference PlaceSiem Reap, Cambodia
PublisherSpringer
Abstract

The aim of aspect-level sentiment classification is to identify the sentiment polarity of a sentence about a target aspect. Existing methods model the context sequence with recurrent network and employ attention mechanism to generate aspect-specific representations. In this paper, we introduce a novel mechanism called Conv-Attention, which can model the sequential information of context words and generate the aspect-specific attention at the same time via a convolution operation. Based on the new mechanism, we design a new framework for aspect-level sentiment classification called Conv-Attention Network (CAN). Compared to the previous attention-based recurrent models, the Conv-Attention Network can compute much faster. Extensive experimental results show that our model achieves the state-of-the-art performance while saving considerable time in model training and inferring.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26110
Collection数字内容技术与服务研究中心_新媒体服务与管理技术
Corresponding AuthorZhang Guixuan
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Yi Qian,Liu Jie,Zhang Guixuan,et al. Aspect-Level Sentiment Classification with Conv-Attention Mechanism[C]:Springer,2018.
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