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Aspect Oriented Opinion Extraction On Semeval
Métriques
Laptop 2014 (F1)
Restaurant 2014 (F1)
Restaurant 2015 (F1)
Restaurant 2016 (F1)
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | |||||
|---|---|---|---|---|---|
| BARTABSA | 80.55 | 85.38 | 80.52 | 87.92 | A Unified Generative Framework for Aspect-Based Sentiment Analysis |
| Dual-MRC | 79.90 | 83.73 | 74.50 | 83.33 | A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis |
| ONG | 75.77 | 82.33 | 78.81 | 86.01 | Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning |
| LOTN | 72.02 | 82.21 | 73.29 | 83.62 | Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction |
| IOG | 71.35 | 80.02 | 73.25 | 81.69 | Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling |
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