GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
This addresses the challenge of disambiguating word senses in natural language processing, with incremental improvements over existing methods.
The paper tackled the problem of Word Sense Disambiguation by better leveraging gloss knowledge in a supervised neural system, resulting in an approach that outperforms state-of-the-art systems on benchmark datasets.
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.