Leveraging Only the Category Name for Aspect Detection through Prompt-based Constrained Clustering (2022.findings-emnlp)
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| Challenge: | Aspect category detection (ACD) aims to automatically identify user-concerned aspects from online reviews. |
| Approach: | They propose a method that relies on the category name of each aspect and a pretrained language model to generate constraints for clustering. |
| Outcome: | The proposed framework performs better than existing weakly supervised methods on nine benchmark datasets. |
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| Challenge: | Current weakly supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. |
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Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews (2022.naacl-industry)
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| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
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Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery (2024.naacl-long)
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| Challenge: | Existing aspects target sentiment classification models are not trainable if annotated data are not available. |
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Solving Aspect Category Sentiment Analysis as a Text Generation Task (2021.emnlp-main)
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| Challenge: | Existing methods for Aspect category sentiment analysis use pre-trained language models to learn aspect category-specific representations. |
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Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)
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| Challenge: | Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence. |
| Approach: | They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights . |
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