  We address the problem of automatically acquiring case-frame patterns from
large corpus data. In particular, we view this problem as the problem of
estimating a (conditional) distribution over a partition of words, and propose
a new generalization method based on the MDL (Minimum Description Length)
principle. In order to assist with the efficiency, our method makes use of an
existing thesaurus and restricts its attention on those partitions that are
present as `cuts' in the thesaurus tree, thus reducing the generalization
problem to that of estimating the `tree cut models' of the thesaurus. We then
give an efficient algorithm which provably obtains the optimal tree cut model
for the given frequency data, in the sense of MDL. We have used the case-frame
patterns obtained using our method to resolve pp-attachment ambiguity.Our
experimental results indicate that our method improves upon or is at least as
effective as existing methods.
