Aspect Oriented Opinion Extraction On Semeval
Metrics
Laptop 2014 (F1)
Restaurant 2014 (F1)
Restaurant 2015 (F1)
Restaurant 2016 (F1)
Results
Performance results of various models on this benchmark
Model Name | 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 | - |
0 of 5 row(s) selected.