Kazakov, D. and Manandhar, S. (2001) Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming. Machine Learning, 43 (1-2). pp. 121-162. ISSN 0885-6125Full text not available from this repository.
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 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. 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.
|Institution:||The University of York|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||York RAE Import|
|Date Deposited:||27 Mar 2009 11:41|
|Last Modified:||27 Mar 2009 11:41|