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.Comment: PhD. Thesis, Univ. of Tokyo, July 1998; latex file, eps figures; 136 pages, page numbers do not comfort to the original; ps font change

A Probabilistic Approach to Lexical Semantic Knowledge Acquisition and Structural Disambiguation

Structural disambiguation in sentence analysis is still a central problem in natural language processing. Past researches have verified that using lexical semantic knowledge can, to a quite large extent, cope with this problem. Although there have been many studies conducted in the past to address the lexical knowledge acquisition problem, further investigation, especially that based on a principled methodology is still needed, and this is, in fact, the problem I address in this thesis