  Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous
word in a particular context. Traditional supervised methods rarely take into
consideration the lexical resources like WordNet, which are widely utilized in
knowledge-based methods. Recent studies have shown the effectiveness of
incorporating gloss (sense definition) into neural networks for WSD. However,
compared with traditional word expert supervised methods, they have not
achieved much improvement. In this paper, we focus on how to better leverage
gloss knowledge in a supervised neural WSD system. We construct context-gloss
pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained
BERT model on SemCor3.0 training corpus and the experimental results on several
English all-words WSD benchmark datasets show that our approach outperforms the
state-of-the-art systems.
