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Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis
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Liang, Bin, Yin, Rongdi, Gui, Lin, Du, Jiachen and Xu, Ruifeng (2020) Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: COLING 2020, Barcelona, Spain (Online), 8-13 Dec 2020. Published in: Proceedings of the 28th International Conference on Computational Linguistics pp. 150-161. doi:10.18653/v1/2020.coling-main.13
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Official URL: http://dx.doi.org/10.18653/v1/2020.coling-main.13
Abstract
In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.
Item Type: | Conference Item (Paper) | |||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Sentiment analysis, Computational linguistics -- Network analysis , Neural networks (Computer science), Natural language processing (Computer science) | |||||||||||||||||||||
Journal or Publication Title: | Proceedings of the 28th International Conference on Computational Linguistics | |||||||||||||||||||||
Publisher: | International Committee on Computational Linguistics | |||||||||||||||||||||
Book Title: | Proceedings of the 28th International Conference on Computational Linguistics | |||||||||||||||||||||
Official Date: | 2020 | |||||||||||||||||||||
Dates: |
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Page Range: | pp. 150-161 | |||||||||||||||||||||
DOI: | 10.18653/v1/2020.coling-main.13 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
Date of first compliant deposit: | 3 March 2021 | |||||||||||||||||||||
Date of first compliant Open Access: | 5 March 2021 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||||||||
Title of Event: | COLING 2020 | |||||||||||||||||||||
Type of Event: | Conference | |||||||||||||||||||||
Location of Event: | Barcelona, Spain (Online) | |||||||||||||||||||||
Date(s) of Event: | 8-13 Dec 2020 | |||||||||||||||||||||
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