Target Aspect Sentiment Detection on Rest 2016
72.76F1 ScoreMVP
Evaluation Results
| Method | Links | |
|---|---|---|
| MVPData=full-data2023.05 | 72.76 | |
| PARAPHRASE2021.10 | 71.97 | |
| ParaphraseScenario=Full human set, # Train=full2026.03 | 71.97 | |
| DLOScenario=Full human set, # Train=full2026.03 | 71.79 | |
| Gemma-3-27B ICLScenario=50 annotated examples, # Train=502026.03 | 68.53 | |
| MVP (transfer)Data=few-shot2023.05 | 68.49 | |
| GAS2021.10 | 68.31 | |
| MEJD2021.10 | 67.66 | |
| Gemma-3-27B ICLScenario=10 annotated examples, # Train=102026.03 | 66.75 | |
| TAS-CRF2021.10 | 65.89 | |
| TAS-TO2021.10 | 65.44 | |
| LA-ABSA w/ ParaphraseScenario=10 annotated examples, # Train=full2026.03 | 62.74 | |
| LA-ABSA w/ DLOScenario=10 annotated examples, # Train=full2026.03 | 62.37 | |
| LA-ABSA w/ ParaphraseScenario=50 annotated examples, # Train=full2026.03 | 62.2 | |
| LA-ABSA w/ DLOScenario=50 annotated examples, # Train=full2026.03 | 62.03 | |
| LA-ABSA w/ ParaphraseScenario=0 annotated examples, # Train=full2026.03 | 47.73 | |
| LA-ABSA w/ DLOScenario=0 annotated examples, # Train=full2026.03 | 47.41 | |
| ChatGPTData=few-shot2023.05 | 46.51 | |
| EDAScenario=50 annotated examples, # Train=5502026.03 | 45.72 | |
| Gemma-3-27B (0-shot)Scenario=0 annotated examples, # Train=02026.03 | 45.51 | |
| QAIEScenario=50 annotated examples, # Train=141.82026.03 | 45.09 | |
| DLOScenario=50 annotated examples, # Train=502026.03 | 43.95 | |
| DS²-ABSAScenario=50 annotated examples, # Train=21,7k2026.03 | 43.53 | |
| Brun and Nikoulina2021.10 | 38.1 | |
| ParaphraseScenario=50 annotated examples, # Train=502026.03 | 35.87 | |
| ChatGPTData=zero-shot2023.05 | 34.08 | |
| DS²-ABSAScenario=10 annotated examples, # Train=21,1k2026.03 | 32.09 | |
| EDAScenario=10 annotated examples, # Train=19.162026.03 | 19.16 | |
| DLOScenario=10 annotated examples, # Train=102026.03 | 13.59 | |
| QAIEScenario=10 annotated examples, # Train=12.372026.03 | 12.37 | |
| ParaphraseScenario=10 annotated examples, # Train=102026.03 | 6.66 |