This result indicates that the model performs well when trained on a small corpus and that its good performance cannot be attributed solely to corpus size.
it also suggests that a large increase in corpus size is necessary to obtain substantial improvements with the present sense ranking strategy, which uses distributional similarity as a corrective for taxonomy-based similarity: Accuracy increases by approximately 4% when our corpus size increases by a factor of 20.