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        <title>Projection-based Coreference Resolution Using Deep Syntax</title>
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    <name type="personal">
        <namePart type="given">Michal</namePart>
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        <namePart type="given">Anna</namePart>
        <namePart type="family">Nedoluzhko</namePart>
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    <name type="personal">
        <namePart type="given">Zdeněk</namePart>
        <namePart type="family">Žabokrtský</namePart>
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            <title>Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)</title>
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        <name type="personal">
            <namePart type="given">Maciej</namePart>
            <namePart type="family">Ogrodniczuk</namePart>
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        <name type="personal">
            <namePart type="given">Vincent</namePart>
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    <abstract>The paper describes the system for coreference resolution in German and Russian, trained exclusively on coreference relations project ed through a parallel corpus from English. The resolver operates on the level of deep syntax and makes use of multiple specialized models. It achieves 32 and 22 points in terms of CoNLL score for Russian and German, respectively. Analysis of the evaluation results show that the resolver for Russian is able to preserve 66% of the English resolver’s quality in terms of CoNLL score. The system was submitted to the Closed track of the CORBON 2017 Shared task.</abstract>
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    <identifier type="doi">10.18653/v1/W17-1508</identifier>
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%0 Conference Proceedings
%T Projection-based Coreference Resolution Using Deep Syntax
%A Novák, Michal
%A Nedoluzhko, Anna
%A Žabokrtský, Zdeněk
%Y Ogrodniczuk, Maciej
%Y Ng, Vincent
%S Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F novak-etal-2017-projection
%X The paper describes the system for coreference resolution in German and Russian, trained exclusively on coreference relations project ed through a parallel corpus from English. The resolver operates on the level of deep syntax and makes use of multiple specialized models. It achieves 32 and 22 points in terms of CoNLL score for Russian and German, respectively. Analysis of the evaluation results show that the resolver for Russian is able to preserve 66% of the English resolver’s quality in terms of CoNLL score. The system was submitted to the Closed track of the CORBON 2017 Shared task.
%R 10.18653/v1/W17-1508
%U https://aclanthology.org/W17-1508
%U https://doi.org/10.18653/v1/W17-1508
%P 56-64

Markdown (Informal)

[Projection-based Coreference Resolution Using Deep Syntax](https://aclanthology.org/W17-1508) (Novák et al., CORBON 2017)

• Projection-based Coreference Resolution Using Deep Syntax (Novák et al., CORBON 2017)

ACL

• Michal Novák, Anna Nedoluzhko, and Zdeněk Žabokrtský. 2017. Projection-based Coreference Resolution Using Deep Syntax. In Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017), pages 56–64, Valencia, Spain. Association for Computational Linguistics.