If vectors capture how words are used, then differences between vectors might capture relations. The classic example from Mikolov and colleagues (2013): in some word-vector sets, king − man + woman lands near queen.

The recipe: compute the vector a − b + c entry by entry, then search the vocabulary for the nearest vector, leaving out the three query words. Without that exclusion the nearest vector is often one of the inputs, such as “king” itself.