Bootstrapping morphological analysis of gĩkũyũ using unsupervised maximum entropy learning

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 Gı̃kũyũ. 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.