White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

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

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 0885-6125

Full text not available from this repository.

Abstract

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.

Item Type: Article
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
Published Version: http://dx.doi.org/10.1023/A:1007629103294
Status: Published
Publisher: Springer Verlag
Identification Number: 10.1023/A:1007629103294
URI: http://eprints.whiterose.ac.uk/id/eprint/6986

Actions (login required)

View Item View Item