We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and conducting syntactic disambiguation by using the acquired word classes. We view the clustering problem as that of estimating a class-based probability distribution specifying the joint probabilities of word pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL) principle for estimating such a probability model. Our clustering method is a natural extension of that proposed in Brown, Della Pietra, deSouza, Lai and Mercer (1992). We next propose a syntactic disambiguation method which combines the use of automatically constructed word classes and that of a hand-made thesaurus. The overall disambiguation accuracy achieved by our method is 88.2%, which compares favorably against the accuracies obtained by the state-of-the-art disambiguation methods.
['Hang Li']
Word clustering and disambiguation based on co-occurrence data