  We address the problem of clustering words (or constructing a thesaurus)
based on co-occurrence data, and using the acquired word classes to improve the
accuracy of syntactic disambiguation. We view this problem as that of
estimating a joint probability distribution specifying the joint probabilities
of word pairs, such as noun verb pairs. We propose an efficient algorithm based
on the Minimum Description Length (MDL) principle for estimating such a
probability distribution. Our method is a natural extension of those proposed
in (Brown et al 92) and (Li & Abe 96), and overcomes their drawbacks while
retaining their advantages. We then combined this clustering method with the
disambiguation method of (Li & Abe 95) to derive a disambiguation method that
makes use of both automatically constructed thesauruses and a hand-made
thesaurus. The overall disambiguation accuracy achieved by our method is 85.2%,
which compares favorably against the accuracy (82.4%) obtained by the
state-of-the-art disambiguation method of (Brill & Resnik 94).
