Hypernym Discovery On Medical Domain
Métriques
MAP
MRR
P@5
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | MAP | MRR | P@5 | Paper Title | Repository |
|---|---|---|---|---|---|
| vTE | 18.84 | 41.07 | 20.71 | - | - |
| CRIM | 34.05 | 54.64 | 36.77 | CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery | - |
| 300-sparsans | 20.75 | 40.60 | 21.43 | 300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes | - |
| EXPR | 13.77 | 40.76 | 12.76 | EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery | - |
| ADAPT | 8.13 | 20.56 | 8.32 | ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora | - |
| SJTU BCMI | 11.69 | 25.95 | 11.69 | SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings | - |
| MFH | 28.93 | 35.80 | 34.20 | - | - |
| balAPInc | 0.91 | 2.10 | 1.08 | Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection |
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