Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis (2022.acl-long)
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| Challenge: | Structured sentiment analysis is a dependency parsing problem, with imbalanced label distributions and large text spans. |
| Approach: | They propose a novel labeling strategy which contains two sets of token pair labels . they propose tuple extraction problem with a more balanced label distribution . |
| Outcome: | The proposed model outperforms existing models on 5 benchmark datasets in four languages. |
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| Challenge: | Structured sentiment analysis attempts to extract full opinion tuples from a text, but has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. |
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| Challenge: | Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity. |
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Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)
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Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
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