Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
Ramy Eskander, Judith L. Klavans, Smaranda Muresan · 2019
Polysynthetic languages pose a challenge for morphological analysis due to the rootmorpheme complexity and to the word class "squish".In addition, many of these polysynthetic languages are low-resource.We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016).We experiment with four languages from the Uto-Aztecan family.Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages.