Abstract
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.
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Acknowledgments
This paper is supported by the Applied Scientific and Technological Special Project of Department of Science and Technology of Guangdong Province (20168010124010); Natural Science Foundation of Guangdong Province (2015A030310318); Medical Scientific Research Foundation of Guangdong Province (A2015065).
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Ouyang, Z., Su, J. (2018). Dependency Parsing and Attention Network for Aspect-Level Sentiment Classification. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_33
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DOI: https://doi.org/10.1007/978-3-319-99495-6_33
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