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CoRTE: A Corpus of Recognizing Textual Entailment Data Annotated for Coreference and Bridging Relations

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Text, Speech, and Dialogue (TSD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11107))

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Abstract

This paper presents CoRTE, an English corpus annotated with coreference and bridging relations, where the dataset is taken from the main task of recognizing textual entailment (RTE). Our annotation scheme elaborates existing schemes by introducing subcategories. Each coreference and bridging relation has been assigned a category. CoRTE is a useful resource for researchers working on coreference and bridging resolution, as well as recognizing textual entailment (RTE) task. RTE has its applications in many NLP domains. CoRTE would thus provide contextual information readily available to the NLP systems being developed for domains requiring textual inference and discourse understanding. The paper describes the annotation scheme with examples. We have annotated 340 text-hypothesis pairs, consisting of 24,742 tokens and 8,072 markables.

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Notes

  1. 1.

    https://aclweb.org/aclwiki/Textual_Entailment_Portal.

  2. 2.

    https://repeval2017.github.io/shared/.

  3. 3.

    https://sourceforge.net/projects/corte.

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Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK

    Afifah Waseem

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  1. Afifah Waseem
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Correspondence to Afifah Waseem .

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Editors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Aleš Horák

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Ivan Kopeček

  4. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Karel Pala

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Waseem, A. (2018). CoRTE: A Corpus of Recognizing Textual Entailment Data Annotated for Coreference and Bridging Relations. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-00794-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00793-5

  • Online ISBN: 978-3-030-00794-2

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