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

Chen Zhang, Qiuchi Li, Dawei Song

[How to correct problems with metadata yourself]


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
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D19-1464.pdf
Code
 GeneZC/ASGCN +  additional community code
Data
SemEval-2014 Task-4