Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance

Longfeng Li, Haifeng Sun, Qi Qi, Jingyu Wang, Jing Wang, Jianxin Liao


Abstract
Aspect-based sentiment analysis (ABSA) aims to distinguish sentiment polarity of every specific aspect in a given sentence. Previous researches have realized the importance of interactive learning with context and aspects. However, these methods are ill-studied to learn complex sentence with multiple aspects due to overlapped polarity feature. And they do not consider the correlation between aspects to distinguish overlapped feature. In order to solve this problem, we propose a new method called Recurrent Inverse Learning Guided Network (RILGNet). Our RILGNet has two points to improve the modeling of aspect correlation and the selecting of aspect feature. First, we use Recurrent Mechanism to improve the joint representation of aspects, which enhances the aspect correlation modeling iteratively. Second, we propose Inverse Learning Guidance to improve the selection of aspect feature by considering aspect correlation, which provides more useful information to determine polarity. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of RILGNet, and we further prove that RILGNet is state-of-the-art method in multiaspect scenarios.
Anthology ID:
2022.coling-1.599
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6887–6896
Language:
URL:
https://aclanthology.org/2022.coling-1.599
DOI:
Bibkey:
Cite (ACL):
Longfeng Li, Haifeng Sun, Qi Qi, Jingyu Wang, Jing Wang, and Jianxin Liao. 2022. Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6887–6896, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (Li et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.599.pdf