Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing models re-encode the sentence for each aspect or rely on static use of deep representations, leading to redundant computation and limited adaptivity. |
| Approach: | They propose a single-pass inference framework that encodes each sentence once to construct a reusable, depth-ordered substrate. |
| Outcome: | Experiments show that DABS reduces end-to-end computation by 60% in multi-aspect settings. |
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| Challenge: | Existing graph-based approaches to predict sentiment polarity for specific aspect terms rely on predefined pairwise structures to improve expressive capacity. |
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Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text. |
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Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Aspect-term sentiment analysis (ATSA) identifies fine-grained sentiments towards specific aspects of text. |
| Approach: | They propose a pipeline to predict fine-grained sentiments for specific aspects of text . it decomposes the learning problem into multiple view subproblems and dynamically selects and constructs features with reinforcement learning. |
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End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
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Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | generative methods have shown promising results for extracting sentiment quadruplets . compound sentences can contain multiple quadroutlets, making extraction difficult . |
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A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks. |
| Approach: | They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. |
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PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
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Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
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| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
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Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-term sentiment analysis (ATSA) is a fine-grained task that aims to infer the sentiment towards the given aspect-terms. |
| Approach: | They propose a novel ATSA method that is interpretable and has high accuracy . they propose SILTN, which is a neurosymbolic formalism, to improve the accuracy based on syntax knowledge distillation. |
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Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triple Extraction (ASTE) is an advanced natural language processing task. |
| Approach: | They propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model which maximizes syntactical and semantic relationships among words. |
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