Now, when we compose these vectors using the multiplicative model and the components definition in (4), we obtain: And by Bayes' theorem: Assuming w 1 and w 2 are independent and applying Bayes' theorem again, h i becomes: By comparing to (4), we can see that the expression on the right hand side gives us something akin to the vector components we would expect when our target is the co-occurrence of w 1 and w 2 .
for the multiplicative model, the combined vector h i can be thought of as an approximation to a vector representing the distributional properties of the phrase w 1 w 2 .