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        <title>Modeling Morphological Typology for Unsupervised Learning of Language Morphology</title>
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        <namePart type="given">Hongzhi</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology. By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology. The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems. We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.</abstract>
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%0 Conference Proceedings
%T Modeling Morphological Typology for Unsupervised Learning of Language Morphology
%A Xu, Hongzhi
%A Kodner, Jordan
%A Marcus, Mitchell
%A Yang, Charles
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-modeling
%X This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology. By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology. The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems. We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.
%R 10.18653/v1/2020.acl-main.596
%U https://aclanthology.org/2020.acl-main.596
%U https://doi.org/10.18653/v1/2020.acl-main.596
%P 6672-6681

Markdown (Informal)

[Modeling Morphological Typology for Unsupervised Learning of Language Morphology](https://aclanthology.org/2020.acl-main.596) (Xu et al., ACL 2020)

• Modeling Morphological Typology for Unsupervised Learning of Language Morphology (Xu et al., ACL 2020)

ACL

• Hongzhi Xu, Jordan Kodner, Mitchell Marcus, and Charles Yang. 2020. Modeling Morphological Typology for Unsupervised Learning of Language Morphology. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6672–6681, Online. Association for Computational Linguistics.