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Word Sense Disambiguation On Semeval 2007
Métriques
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Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| SemCor+WNGC, hypernyms | 73.4 | Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation |
| SemCor+WNGT, vocabulary reduced, ensemble | 66.81 | Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships |
| LSTM (T:SemCor) | 64.2 | Semi-supervised Word Sense Disambiguation with Neural Models |
| LSTMLP (T:SemCor, U:OMSTI) | 63.7 | Semi-supervised Word Sense Disambiguation with Neural Models |
| LSTMLP (T:SemCor, U:1K) | 63.5 | Semi-supervised Word Sense Disambiguation with Neural Models |
| LSTMLP (T:OMSTI, U:1K) | 63.3 | Semi-supervised Word Sense Disambiguation with Neural Models |
| kNN-BERT + POS (training corpus: SemCor) | 63.17 | Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings |
| kNN-BERT | 60.94 | Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings |
| LSTM (T:OMSTI) | 60.7 | Semi-supervised Word Sense Disambiguation with Neural Models |
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