The underlying assump-tion is that a vector, in order to be a good representation of the meaning of the corresponding word, should lie in a region of semantic space populated by intuitively similar meanings, e.g., we are more likely to have captured the meaning of car if the NN of its vector is the automobile vector rather than potato.
to measure the quality of a given vector, we can look at the average similarity score provided by humans when comparing this very vector with its own NNs.