(Un)supervised morphological segmentation for low-resource languages


There exists a growing asymmetry between the tools available to computational linguists on the one hand and language documentarians and linguists researching low-resource languages on the other. To this end, the current project details the application of modern natural language processing tools to the task of morphological segmentation, a necessary primary step in morphological description and analysis. Results from supervised and unsupervised versions of a Bayesian statistical learning model used for the task of morphological segmentation of a small dataset consisting of the inflectional paradigms of a handful of Bardi (Nyulnyulan; Australia) verbs are outlined. Moreover, implications for the use of such models for related documentary and analytic tasks are discussed.

(under review) In Language Documentation and Description
Parker Brody
PhD Candidate

Yale University Department of Linguistics. Computational linguistics, Historical linguistics, Morphological Theory.