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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 11–20 of 52 papers

TitleStatusHype
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple PredictionCode0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction—0
Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet ExtractionCode0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian LanguageCode0
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet ExtractionCode1
A semantically enhanced dual encoder for aspect sentiment triplet extractionCode1
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—0
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet ExtractionCode1
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