Zheng, Y., Zhang, R., Mensah, S. orcid.org/0000-0003-0779-5574 et al. (1 more author) (2020) Replicate, walk, and stop on syntax : an effective neural network model for aspect-level sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. Thirty-Fourth AAAI Conference on Artificial Intelligence, 07-12 Feb 2020, New York, NY, USA. Association for the Advancement of Artificial Intelligence (AAAI) , pp. 9685-9692. ISBN 9781577358350
Abstract
Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, Association for the Advancement of Artificial Intelligence. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Feb 2021 12:18 |
Last Modified: | 03 Feb 2021 12:18 |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence (AAAI) |
Refereed: | Yes |
Identification Number: | 10.1609/aaai.v34i05.6517 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169663 |