Work

Unsupervised morphological segmentation in a language with reduplication

Year: 2022

Type: conference-paper

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 Māori, 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.

Source: SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology (SIGMORPHON)

DOI: 10.18653/v1/2022.sigmorphon-1.2

Authors: Simon Todd, Annie Huang, Jeremy Needle, Jennifer Hay, Jeanette King

Institutions: University of California, Santa Barbara, University of Canterbury

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Marsden Fund

Award: G4940780021