A Probabilistic Approach to Lexical Semantic Knowledge Acquisition and S   tructural Disambiguation
  In this thesis, I address the problem of automatically acquiring lexical semantic knowledge, especially that of case frame patterns, from large corpus data and using the acquired knowledge in structural disambiguation. The approach I adopt has the following characteristics: (1) dividing the problem into three subproblems: case slot generalization, case dependency learning, and word clustering (thesaurus construction). (2) viewing each subproblem as that of statistical estimation and defining probability models for each subproblem, (3) adopting the Minimum Description Length (MDL) principle as learning strategy, (4) employing efficient learning algorithms, and (5) viewing the disambiguation problem as that of statistical prediction. Major contributions of this thesis include: (1) formalization of the lexical knowledge acquisition problem, (2) development of a number of learning methods for lexical knowledge acquisition, and (3) development of a high-performance disambiguation method. 
Hang LI (NEC Corporation)