  We address the problem of automatically acquiring case frame patterns
(selectional patterns) from large corpus data. In particular, we propose a
method of learning dependencies between case frame slots. We view the problem
of learning case frame patterns as that of learning multi-dimensional discrete
joint distributions, where random variables represent case slots. We then
formalize the dependencies between case slots as the probabilistic dependencies
between these random variables. Since the number of parameters in a
multi-dimensional joint distribution is exponential, it is infeasible to
accurately estimate them in practice. To overcome this difficulty, we settle
with approximating the target joint distribution by the product of low order
component distributions, based on corpus data. In particular we propose to
employ an efficient learning algorithm based on the MDL principle to realize
this task. Our experimental results indicate that for certain classes of verbs,
the accuracy achieved in a disambiguation experiment is improved by using the
acquired knowledge of dependencies.
