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Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.

Papers

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Showing 31–40 of 52 papers

TitleStatusHype
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment AnalysisCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet ExtractionCode1
A Span-level Bidirectional Network for Aspect Sentiment Triplet ExtractionCode1
Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction—0
SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT—0
Aspect-Sentiment-Multiple-Opinion Triplet ExtractionCode0
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet ExtractionCode1
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
Towards Generative Aspect-Based Sentiment AnalysisCode1
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