Among the various approaches to WSD, the supervised learning approach is the most successful to date. In this approach, we first collect a corpus in which each occurrence of an ambiguous word w has been manually annotated with the correct sense, according to some existing sense inventory in a dictionary. This annotated corpus then serves as the training material for a learning algorithm. After training, a model is automatically learned and it is used to assign the correct sense to any previously unseen occurrence of w in a new context. .
