Abstract
Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme 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.- Anthology ID:
- W19-4222
- Volume:
- Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Garrett Nicolai, Ryan Cotterell
- Venue:
- ACL
- SIG:
- SIGMORPHON
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 189–195
- Language:
- URL:
- https://aclanthology.org/W19-4222
- DOI:
- 10.18653/v1/W19-4222
- Cite (ACL):
- Ramy Eskander, Judith Klavans, and Smaranda Muresan. 2019. Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages. In Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 189–195, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages (Eskander et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-4222.pdf