Learning Dependencies between Case Frame Slots

Details Learning Dependencies between Case Frame Slots Learning Dependencies between Case Frame Slots

1996-05-12

arxiv.org/abs/cmp-lg/9605013v2

Computer Science

Computation and Language

12 pages, LaTex source, 2 eps figures, uses colap.sty and epsf.sty ; an extended abstract of this paper appeared in Proc. of C

Scientific paper

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.

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