Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

Chen Zhang, Qiuchi Li, Dawei Song

Abstract
Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.
Anthology ID:
D19-1464
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4568–4578
Language:
URL:
https://aclanthology.org/D19-1464
DOI:
10.18653/v1/D19-1464
Bibkey:
Cite (ACL):
Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4568–4578, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (Zhang et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/D19-1464.pdf
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