Bootstrapping morphological analysis of Gĩkũyũ using Maximum Entropy Learning

TitleBootstrapping morphological analysis of Gĩkũyũ using Maximum Entropy Learning
Publication TypeConference Paper
Year of Publication2007
AuthorsDe Pauw, Guy, and Wagacha Peter W.
BooktitleProceedings of the eighth INTERSPEECH conference
LocationAntwerp, Belgium
Abstract

This paper describes a proof-of-the-principle experiment in which maximum entropy learning is used for the automatic induction of shallow morphological features for the resourcescarce Bantu language of Gĩkũyũ. This novel approach circumvents the limitations of typical unsupervised morphological induction methods that employ minimum-edit distance metrics to establish morphological similarity between words. The experimental results show that the unsupervised maximum entropy learning approach compares favorably to those of the established AutoMorphology method.

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