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        <title>Voted-Perceptron Approach for Kazakh Morphological Disambiguation</title>
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    <name type="personal">
        <namePart type="given">Gulmira</namePart>
        <namePart type="family">Tolegen</namePart>
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    <name type="personal">
        <namePart type="given">Rustam</namePart>
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            <title>Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)</title>
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        <name type="personal">
            <namePart type="given">Dorothee</namePart>
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        <name type="personal">
            <namePart type="given">Laurent</namePart>
            <namePart type="family">Besacier</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sakriani</namePart>
            <namePart type="family">Sakti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Soria</namePart>
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            <publisher>European Language Resources association</publisher>
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    <abstract>This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.</abstract>
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        <url>https://aclanthology.org/2020.sltu-1.36</url>
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        <date>2020-05</date>
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%0 Conference Proceedings
%T Voted-Perceptron Approach for Kazakh Morphological Disambiguation
%A Tolegen, Gulmira
%A Toleu, Alymzhan
%A Mussabayev, Rustam
%Y Beermann, Dorothee
%Y Besacier, Laurent
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
%D 2020
%8 May
%I European Language Resources association
%C Marseille, France
%@ 979-10-95546-35-1
%G English
%F tolegen-etal-2020-voted
%X This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.
%U https://aclanthology.org/2020.sltu-1.36
%P 258-264

Markdown (Informal)

[Voted-Perceptron Approach for Kazakh Morphological Disambiguation](https://aclanthology.org/2020.sltu-1.36) (Tolegen et al., SLTU 2020)

• Voted-Perceptron Approach for Kazakh Morphological Disambiguation (Tolegen et al., SLTU 2020)

ACL

• Gulmira Tolegen, Alymzhan Toleu, and Rustam Mussabayev. 2020. Voted-Perceptron Approach for Kazakh Morphological Disambiguation. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 258–264, Marseille, France. European Language Resources association.