Let's go through a practical example and see how skip-gram models will work in this situation:
the quick brown fox jumped over the lazy dog
First off, we need to make a dataset of words and their corresponding context. Defining the context is up to us, but it has to make sense. So, we'll take a window around the target word and take a word from the right and another from the left.
By following this contextual technique, we will end up with the following set of words and their corresponding context:
([the, brown], quick), ([quick, fox], brown), ([brown, jumped], fox), ...
The generated words and their corresponding context will be represented as pairs of (context, target). The idea of skip-gram models is the inverse of CBOW ones. In the skip- gram model, we will try to predict the context of the word based on its target...
