Because we are able to determine not only the distance between each two senses of the input terms, but also the distance between each input term and all of its senses, we use this information to fine tune the computed similarity scores and use ranking as a criterion to grade senses' relevance.
we hypothesize that the relevance of senses for a given term can be helpful for the computation of similarity scores, so we devised a measure that also accounts for the ranking of distances between senses and seed term.