Unsupervised discovery of phonological categories through supervised learning of morphological rules

Authors:
Walter Daelemans;Peter Berck;Steven Gillis
Affiliations:
CL & AI, Tilburg University, Tilburg, The Netherlands;Linguistics, University of Antwerp, Wilrijk, Belgium;Linguistics, University of Antwerp, Wilrijk, Belgium
Venue:
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Year:
1996

Citing 3
Cited 3

Toward memory-based reasoning

Communications of the ACM - Special issue on parallelism
C4.5: programs for machine learning

C4.5: programs for machine learning
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features

Machine Learning

Automatic acquisition of two-level morphological rules

ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A procedure for multi-class discrimination and some linguistic applications

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning

Research on Language and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a case study in the application of symbolic machine learning techniques for the discovery of linguistic rules and categories. A supervised rule induction algorithm is used to learn to predict the correct diminutive suffix given the phonological representation of Dutch nouns. The system produces rules which are comparable to rules proposed by linguists. Furthermore, in the process of learning this morphological task, the phonemes used are grouped into phonologically relevant categories. We discuss the relevance of our method for linguistics and language technology.