  We compare four similarity-based estimation methods against back-off and
maximum-likelihood estimation methods on a pseudo-word sense disambiguation
task in which we controlled for both unigram and bigram frequency. The
similarity-based methods perform up to 40% better on this particular task. We
also conclude that events that occur only once in the training set have major
impact on similarity-based estimates.
