@inproceedings{eskander-etal-2019-unsupervised,
title = "Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages",
author = "Eskander, Ramy and
Klavans, Judith and
Muresan, Smaranda",
booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4222",
doi = "10.18653/v1/W19-4222",
pages = "189--195",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
%A Eskander, Ramy
%A Klavans, Judith
%A Muresan, Smaranda
%S Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F eskander-etal-2019-unsupervised
%X 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.
%R 10.18653/v1/W19-4222
%U https://aclanthology.org/W19-4222
%U https://doi.org/10.18653/v1/W19-4222
%P 189-195
Markdown (Informal)
[Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages](https://aclanthology.org/W19-4222) (Eskander et al., 2019)
ACL