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Unsupervised morphological segmentation in a language with reduplication

Simon Todd,Annie Huang,2 Authors,J. King

2022 · DOI: 10.18653/v1/2022.sigmorphon-1.2
Special Interest Group on Computational Morphology and Phonology Workshop · 3 Citations

TLDR

It is shown that incorporating templates improves Morfessor’s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates.

Abstract

We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process. Through a detailed investigation that applies the model to Maori, the ̄ Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor’s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates. We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.