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.
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Acknowledgments
This work has been supported by the National Key R&D Program of China under Grant NO.2017YFB1401000 and the Key Laboratory of Digital Rights Services, which is one of the National Science and Standardization Key Labs for Press and Publication Industry.
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Yi, Q., Liu, J., Zhang, G., Zhang, S. (2018). Aspect-Level Sentiment Classification with Conv-Attention Mechanism. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_20
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DOI: https://doi.org/10.1007/978-3-030-04212-7_20
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