Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programing

摘要

This article presents a combination of unsupervised and supervised learning techniques for the gen- eration of word segmentation rules from a raw list of words. First, a language bias for word segmentation is introduced and a simple genetic algorithm is used in the search for a segmentation that corresponds to the best bias value. In the second phase, the words segmented by the genetic algorithm are used as an input for the first order decision list learner CLOG.