According to Pedersen (2010), the supervised learning algorithm that underlies this evaluation method tends to converge to the Most Frequent Sense (MFS) baseline, because the number of senses that the classifier assigns to the test instances is rather low.
the quality of the proposed clustering solution is evaluated by comparing the two groupings and measuring their similarity.