In the distributional similarity scheme each word w is represented by a feature vector, where an entry in the vector corresponds to a feature f. Each feature represents another word (or term) with which w co-occurs, and possibly specifies also the syntactic relation between the two words.
this may yield high similarity scores for less similar word pairs, while missing other correct similarities.