Most existing data-driven approaches for Word sense disambiguation (WSD) build word-specific classifiers to predict the right sense of a word instance, which lack the capability to generalize across words and therefore require sufficient sense-annotated data for every word (de Lacalle and Agirre, 2015) in order to disambiguate them well.
the model's performance decreases significantly when there is a lack of training data for a word or some of its senses.