Given a pair of word vectors v and v , and any desired "base" vector similarity measure sim (e.g.
sim Lin ), we compute a topic-biased similarity score for each LDA topic t, denoted by ) is computed by applying the original similarity measure over topic-biased versions of v and v , denoted by v t and v t : each value in the biased vector, v t (w), is obtained by weighing the original value v(w) by the relevance of the topic t to the argument word w within d v .