Unsupervised Morphological Segmentation for Low Resource Polysynthetic Languages
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.