As with most machine learning problems, lack of labelled data is a common problem in building disambiguation models. thus, we need unsupervise+
A popular unsupervised algorithm used for word sense disambiguation is the Lesk algoritham.
There are various ways in which you can use the lesk algorithm, Apart from what the professor has discussed, you can just take the definitions corresponding to the different senses of the ambiguous word and see which definition overlaps maximum with the
neighbouring words of the ambiguous word. The sense which has the maximum overlap with the surrounding words is then chosen as the ‘correct sense’.
Let’s now use lesk algorithm to disambiguate the word ‘bank‘ in a text.
Recall that WordNet has a network of synonyms called synset for individual words.
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