STM assumes that different word vector specializations emphasize different subsets of semantic properties of words that are more informative for predicting some lexico-semantic relations than others.
we assume that a particular specialization function f (i) S can be trained to transform the input vectors x 1 and x 2 into vectors that encode properties suitable for predicting a particular relation, e.g., hypernymy.