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    <titleInfo>
        <title>Dependency Model using Posterior Context</title>
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        <namePart type="given">Kiyotaka</namePart>
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    <name type="personal">
        <namePart type="given">Masaki</namePart>
        <namePart type="family">Murata</namePart>
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    <name type="personal">
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        <dateIssued>2000-feb&quot; 23-25&quot;</dateIssued>
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            <title>Proceedings of the Sixth International Workshop on Parsing Technologies</title>
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    <abstract>We describe a new model for dependency structure analysis. This model learns the relationship between two phrasal units called bunsetsus as three categories; ‘between’, ‘dependent’, and ‘beyond’, and estimates the dependency likelihood by considering not only the relationship between two bunsetsus but also the relationship between the left bunsetsu and all of the bunsetsus to its right. We implemented this model based on the maximum entropy model. When using the Kyoto University corpus, the dependency accuracy of our model was 88%, which is about 1% higher than that of the conventional model using exactly the same features.</abstract>
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        <url>https://aclanthology.org/2000.iwpt-1.43</url>
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        <date>2000-feb&quot; 23-25&quot;</date>
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            <start>321</start>
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%0 Conference Proceedings
%T Dependency Model using Posterior Context
%A Uchimoto, Kiyotaka
%A Murata, Masaki
%A Sekine, Satoshi
%A Isahara, Hitoshi
%S Proceedings of the Sixth International Workshop on Parsing Technologies
%D 2000
%8 feb" 23 25"
%I Association for Computational Linguistics
%C Trento, Italy
%F uchimoto-etal-2000-dependency
%X We describe a new model for dependency structure analysis. This model learns the relationship between two phrasal units called bunsetsus as three categories; ‘between’, ‘dependent’, and ‘beyond’, and estimates the dependency likelihood by considering not only the relationship between two bunsetsus but also the relationship between the left bunsetsu and all of the bunsetsus to its right. We implemented this model based on the maximum entropy model. When using the Kyoto University corpus, the dependency accuracy of our model was 88%, which is about 1% higher than that of the conventional model using exactly the same features.
%U https://aclanthology.org/2000.iwpt-1.43
%P 321-322

Markdown (Informal)

[Dependency Model using Posterior Context](https://aclanthology.org/2000.iwpt-1.43) (Uchimoto et al., IWPT 2000)

• Dependency Model using Posterior Context (Uchimoto et al., IWPT 2000)

ACL

• Kiyotaka Uchimoto, Masaki Murata, Satoshi Sekine, and Hitoshi Isahara. 2000. Dependency Model using Posterior Context. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 321–322, Trento, Italy. Association for Computational Linguistics.