Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming

作     者：Kazakov, D Manandhar, S 

作者机构：Univ York York YO10 5DD N Yorkshire England 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2001年第43卷第1-2期

页      面：121-162页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：CTU Prague ESPRIT Long Term Research Action, (20237) 

主　　题：unsupervised machine learning inductive logic programming natural language word segmentation 

摘      要：This article presents a combination of unsupervised and supervised learning techniques for the generation of word segmentation rules from a raw list of words. First, a language bias for word se mentation 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. The result is a set of first order rules which can be used for segmentation of unseen words. When applied on either the training data or unseen data, these rules produce segmentations which are linguistically meaningful, and to a large degree conforming to the annotation provided.