1 research outputs found
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