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        <title>Unsupervised Morphology Learning with Statistical Paradigms</title>
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        <namePart type="given">Hongzhi</namePart>
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    <abstract>This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Unsupervised Morphology Learning with Statistical Paradigms
%A Xu, Hongzhi
%A Marcus, Mitchell
%A Yang, Charles
%A Ungar, Lyle
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F xu-etal-2018-unsupervised
%X This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.
%U https://aclanthology.org/C18-1005
%P 44-54

Markdown (Informal)

[Unsupervised Morphology Learning with Statistical Paradigms](https://aclanthology.org/C18-1005) (Xu et al., COLING 2018)

• Unsupervised Morphology Learning with Statistical Paradigms (Xu et al., COLING 2018)

ACL

• Hongzhi Xu, Mitchell Marcus, Charles Yang, and Lyle Ungar. 2018. Unsupervised Morphology Learning with Statistical Paradigms. In Proceedings of the 27th International Conference on Computational Linguistics, pages 44–54, Santa Fe, New Mexico, USA. Association for Computational Linguistics.