The most significant difference, however, arose from the limitations due to data sparseness that are inherent in methods based on distributional similarity: the very small size of the set of words that could be corrected.
only malapropisms for which both the error and the correction occurred in the set of 2,000 words for which Weeds had distributional data could be considered; and the ability to detect and correct the malapropism depended on other members of that set also being within the scope of the target word.