Learning dependencies between case frame slots
Hang Li, Naoki Abe · 1996
We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data.In particular, we l)ropose a method of learning dependencies between case frame slots.We view the problem of learning case frame patterns as that of learning a multi-dimensional discrete joint distribution, where random variables represent case slots.We then for-mMize the dependencies between case slots as the probabilislic dependencies between these random variables.Since the number of parameters in a multidimensional joint distribution is exponential in general, 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.