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In Word Sense Disambiguation (WSD), the predominant approach generallyinvolves a supervised system trained on sense annotated corpora. The limitedquantity of such corpora however restricts the coverage and the performance ofthese systems. In this article, we propose a new method that solves theseissues by taking advantage of the knowledge present in WordNet, and especiallythe hypernymy and hyponymy relationships between synsets, in order to reducethe number of different sense tags that are necessary to disambiguate all wordsof the lexical database. Our method leads to state of the art results on mostWSD evaluation tasks, while improving the coverage of supervised systems,reducing the training time and the size of the models, without additionaltraining data. In addition, we exhibit results that significantly outperformthe state of the art when our method is combined with an ensembling techniqueand the addition of the WordNet Gloss Tagged as training corpus.

Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships