The "pure" corpus-based distributional profile (a.k.a.
co-occurrence vector, or word association vector), for some target word u, is biased with soft constraints towards each of the concepts c that list u in the thesaurus, to create a distributional profile that is specific to u in the sense that is most related to the other words listed under c. this method can make more finegrained distinctions than the Mohammad and Hirst method, and yet uses word sense information.