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Word Sense Disambiguation On Semeval 2007 1

Metrics

F1
Unsupervised

Results

Performance results of various models on this benchmark

Model Name
F1
Unsupervised
Paper TitleRepository
ShotgunWSD 2.081.22yesShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing-
kNN-BERT81.20-Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings-
LSTMLP (T:OMSTI, U:1K)83.3-Semi-supervised Word Sense Disambiguation with Neural Models-
LSTM (T:SemCor)82.8-Semi-supervised Word Sense Disambiguation with Neural Models-
LSTM (T:OMSTI)81.1-Semi-supervised Word Sense Disambiguation with Neural Models-
kNN-BERT + POS (training corpus: WNGT)85.32-Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings-
LSTMLP (T:SemCor, U:OMSTI)84.3-Semi-supervised Word Sense Disambiguation with Neural Models-
LSTMLP (T:SemCor, U:1K)83.6-Semi-supervised Word Sense Disambiguation with Neural Models-
SemCor+WNGT, vocabulary reduced, ensemble86.02-Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships-
SemCor+WNGC, hypernyms90.4-Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation-
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Word Sense Disambiguation On Semeval 2007 1 | SOTA | HyperAI