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            <title>Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas</title>
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            <namePart type="given">Manuel</namePart>
            <namePart type="family">Mager</namePart>
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    <abstract>We study the performance of several popular neural part-of-speech taggers from the Universal Dependencies ecosystem on Mayan languages using a small corpus of 1435 annotated K’iche’ sentences consisting of approximately 10,000 tokens, with encouraging results: F₁ scores 93%+ on lemmatisation, part-of-speech and morphological feature assignment. The high performance motivates a cross-language part-of-speech tagging study, where K’iche’-trained models are evaluated on two other Mayan languages, Kaqchikel and Uspanteko: performance on Kaqchikel is good, 63-85%, and on Uspanteko modest, 60-71%. Supporting experiments lead us to conclude the relative diversity of morphological features as a plausible explanation for the limiting factors in cross-language tagging performance, providing some direction for future sentence annotation and collection work to support these and other Mayan languages.</abstract>
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    <identifier type="doi">10.18653/v1/2021.americasnlp-1.6</identifier>
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%0 Conference Proceedings
%T A survey of part-of-speech tagging approaches applied to K’iche’
%A Tyers, Francis
%A Howell, Nick
%Y Mager, Manuel
%Y Oncevay, Arturo
%Y Rios, Annette
%Y Ruiz, Ivan Vladimir Meza
%Y Palmer, Alexis
%Y Neubig, Graham
%Y Kann, Katharina
%S Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F tyers-howell-2021-survey
%X We study the performance of several popular neural part-of-speech taggers from the Universal Dependencies ecosystem on Mayan languages using a small corpus of 1435 annotated K’iche’ sentences consisting of approximately 10,000 tokens, with encouraging results: F₁ scores 93%+ on lemmatisation, part-of-speech and morphological feature assignment. The high performance motivates a cross-language part-of-speech tagging study, where K’iche’-trained models are evaluated on two other Mayan languages, Kaqchikel and Uspanteko: performance on Kaqchikel is good, 63-85%, and on Uspanteko modest, 60-71%. Supporting experiments lead us to conclude the relative diversity of morphological features as a plausible explanation for the limiting factors in cross-language tagging performance, providing some direction for future sentence annotation and collection work to support these and other Mayan languages.
%R 10.18653/v1/2021.americasnlp-1.6
%U https://aclanthology.org/2021.americasnlp-1.6
%U https://doi.org/10.18653/v1/2021.americasnlp-1.6
%P 44-52

Markdown (Informal)

[A survey of part-of-speech tagging approaches applied to K’iche’](https://aclanthology.org/2021.americasnlp-1.6) (Tyers & Howell, AmericasNLP 2021)

• A survey of part-of-speech tagging approaches applied to K’iche’ (Tyers & Howell, AmericasNLP 2021)

ACL

• Francis Tyers and Nick Howell. 2021. A survey of part-of-speech tagging approaches applied to K’iche’. In Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, pages 44–52, Online. Association for Computational Linguistics.