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        <title>Unsupervised morphological segmentation in a language with reduplication</title>
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        <namePart type="given">Annie</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Jennifer</namePart>
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    <name type="personal">
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            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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    <abstract>We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process. Through a detailed investigation that applies the model to Maori, the ̄ Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor’s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates. We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.</abstract>
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    <identifier type="doi">10.18653/v1/2022.sigmorphon-1.2</identifier>
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%0 Conference Proceedings
%T Unsupervised morphological segmentation in a language with reduplication
%A Todd, Simon
%A Huang, Annie
%A Needle, Jeremy
%A Hay, Jennifer
%A King, Jeanette
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%S Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F todd-etal-2022-unsupervised
%X We present an extension of the Morfessor Baseline model of unsupervised morphological segmentation (Creutz and Lagus, 2007) that incorporates abstract templates for reduplication, a typologically common but computationally underaddressed process. Through a detailed investigation that applies the model to Maori, the ̄ Indigenous language of Aotearoa New Zealand, we show that incorporating templates improves Morfessor’s ability to identify instances of reduplication, and does so most when there are multiple minimally-overlapping templates. We present an error analysis that reveals important factors to consider when applying the extended model and suggests useful future directions.
%R 10.18653/v1/2022.sigmorphon-1.2
%U https://aclanthology.org/2022.sigmorphon-1.2
%U https://doi.org/10.18653/v1/2022.sigmorphon-1.2
%P 12-22

Markdown (Informal)

[Unsupervised morphological segmentation in a language with reduplication](https://aclanthology.org/2022.sigmorphon-1.2) (Todd et al., SIGMORPHON 2022)

• Unsupervised morphological segmentation in a language with reduplication (Todd et al., SIGMORPHON 2022)

ACL

• Simon Todd, Annie Huang, Jeremy Needle, Jennifer Hay, and Jeanette King. 2022. Unsupervised morphological segmentation in a language with reduplication. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 12–22, Seattle, Washington. Association for Computational Linguistics.