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        <title>Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages</title>
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            <title>Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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        <name type="personal">
            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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        <name type="personal">
            <namePart type="given">Ryan</namePart>
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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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    <identifier type="doi">10.18653/v1/W19-4222</identifier>
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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
%Y Nicolai, Garrett
%Y Cotterell, Ryan
%S Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2019
%8 August
%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., ACL 2019)

• Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages (Eskander et al., ACL 2019)

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.