The skip-gram model is trained to predict the surrounding words given the current word. To understand how the skip-gram word2vec model works, consider the following example sentence:
I love green eggs and ham.
Assuming a window size of three, this sentence can be broken down into the following sets of (context, word) pairs:
([I, green], love) ([love, eggs], green) ([green, and], eggs) ...
Since the skip-gram model predicts a context word given the center word, we can convert the preceding dataset to one of (input, output) pairs. That is, given an input word, we expect the skip-gram model to predict the output word:
(love, I), (love, green), (green, love), (green, eggs), (eggs, green), (eggs, and), ...
We can also ...
