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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 1–10 of 52 papers

TitleStatusHype
T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction—0
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction—0
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet ExtractionCode0
ASTE Transformer Modelling Dependencies in Aspect-Sentiment Triplet ExtractionCode0
Table-Filling via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction—0
10 Years of Fair Representations: Challenges and Opportunities—0
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation ModelsCode0
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet ExtractionCode0
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