  We study distributional similarity measures for the purpose of improving
probability estimation for unseen cooccurrences. Our contributions are
three-fold: an empirical comparison of a broad range of measures; a
classification of similarity functions based on the information that they
incorporate; and the introduction of a novel function that is superior at
evaluating potential proxy distributions.
