Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)
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| Challenge: | Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature. |
| Approach: | They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects . |
| Outcome: | The proposed method is state-of-the-art in multiaspect scenarios. |
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence. |
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| Challenge: | Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences . |
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Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
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| Challenge: | Aspect-based sentiment analysis models are susceptible to learning spurious correlations between words . a recent study shows that feature engineering is time-consuming and costly . |
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| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
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