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Found 3 papers, 1 papers with code
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
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Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.
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