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
Nom du modèle | Laptop 2014 (F1) | Restaurant 2014 (F1) | Restaurant 2015 (F1) | Restaurant 2016 (F1) | Paper Title | Repository |
|---|---|---|---|---|---|---|
| Dual-MRC | 79.90 | 83.73 | 74.50 | 83.33 | A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis | - |
| BARTABSA | 80.55 | 85.38 | 80.52 | 87.92 | A Unified Generative Framework for Aspect-Based Sentiment Analysis | |
| 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 | |
| ONG | 75.77 | 82.33 | 78.81 | 86.01 | Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning | - |
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