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Distributional similarity is a useful notion in estimating the probabilities of rare joint events. It has been employed both to cluster events according to their distributions, and to directly compute averages of estimates for distributional neighbors of a target event. Here, we examine the tradeoffs between model size and prediction accuracy for cluster-based and nearest neighbors distributional models of unseen events. | Distributional Similarity Models Clustering vs. Nearest Neighbors Lillian Lee Department of Computer Science Cornell University Ithaca NY 14853-7501 lleeỗcs.Cornell.edu Fernando Pereira A247 AT T Labs - Research 180 Park Avenue Florham Park NJ 07932-0971 pereiraSresearch.att.com Abstract Distributional similarity is a useful notion in estimating the probabilities of rare joint events. It has been employed both to cluster events according to their distributions and to directly compute averages of estimates for distributional neighbors of a target event. Here we examine the tradeoffs between model size and prediction accuracy for cluster-based and nearest neighbors distributional models of unseen events. 1 Introduction In many statistical language-processing problems it is necessary to estimate the joint probability or cooccurrence probability of events drawn from two prescribed sets. Data sparseness can make such estimates difficult when the events under consideration are sufficiently fine-grained for instance when they correspond to occurrences of specific words in given configurations. In particular in many practical modeling tasks a substantial fraction of the cooccurrences of interest have never been seen in training data. In most previous work Jelinek and Mercer 1980 Katz 1987 Church and Gale 1991 Ney and Essen 1993 this lack of information is addressed by reserving some mass in the probability model for unseen joint events and then assigning that mass to those events as a function of their marginal frequencies. An intuitively appealing alternative to relying on marginal frequencies alone is to combine estimates of the probabilities of similar events. More specifically a joint event x y would be considered similar to another a y if the distributions of Y given X and Y given x the cooccurrence distributions of X and xr meet an appropriate definition of distributional similarity. For example one can infer that the bigram after ACL-99 is plausible even if it has .
