In order to be able to analyze the impact of the amount of knowledge on the disambiguation performance, the 800 sentences in the training data set were split into 10 different subsets of varying size (from 80 sentences [i.e., 10% of training instances] to the full set of 800 sentences in 10 steps) while at the same time preserving the original sense distribution for all these sets.
many of the WordNet synsets do not contain monosemous terms and the similarity-based approach often needs to look further into the other indirectly related synsets so as to find a suitable pseudosense.