Similarity-Based Methods For Word Sense Disambiguation

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. 1 I n t r o d u c t i o n The problem of data sparseness affects all statistical methods for natural language processing. Even large training sets tend to misrepresent low-probability events, since rare events may not appear in the training corpus at all. We concentrate here on the problem of estimating the probability of unseen word pairs, that is, pairs that do not occur in the training set. Katz's back-off scheme (Katz, 1987), widely used in bigram language modeling, estimates the probability of an unseen bigram by utilizing unigram estimates. This has the undesirable result of assigning unseen bigrams the same probability if they are made up of unigrams of the same frequency. Class-based methods (Brown et al., 1992; Pereira, Tishby, and Lee, 1993; Resnik, 1992) cluster words into classes of similar words, so that one can base the estimate of a word pair's probability on the averaged cooccurrence probability of the classes to which the two words belong. However, a word is therefore modeled by the average behavior of many words, which may cause the given word's idiosyncrasies to be ignored. For instance, the word "red" might well act like a generic color word in most cases, but it has distinctive cooccurrence patterns with respect to words like "apple," "banana," and so