Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)
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| Challenge: | Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency. |
| Approach: | They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities . |
| Outcome: | The proposed framework outperforms the sequence tagging baseline on three benchmark datasets. |
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| Challenge: | Existing research efforts focus on targeting sentiment analysis as a sequence labeling problem, building models that can capture explicit structures in the output space. |
| Approach: | They argue that both implicit and explicit structural information are crucial for building a successful targeted sentiment analysis model. |
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SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020.acl-main)
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| Challenge: | Aspect terms and opinion terms are key problems of fine-grained aspect-based sentiment analysis. |
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Structured Sentiment Analysis as Dependency Graph Parsing (2021.acl-long)
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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 models with span-based labeling have achieved promising results in sentiment analysis. |
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| Challenge: | Existing methods for sentiment classification focus on learning domain-invariant representations . few of them pay attention to domain-specific information, which should also be informative. |
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| Challenge: | Aspect-level sentiment classification (ALSC) is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. |
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Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
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Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)
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| Challenge: | Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span. |
| Approach: | They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features. |
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Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
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Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
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