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Detecting Non-reference and Non-anaphoricity

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Anaphora Resolution

Abstract

In this chapter we discuss proposals concerning the detection of non-referentiality and non-anaphoricity, and the integration of such methods in an anaphora resolution system. We first review in brief a number of proposals on the topics, also covering literature on detecting abstract anaphora and discussing available resources; we then discuss in detail the proposals by Bergsma on expletive detection, and by Poesio et al. and Kabadjov on discourse-new detection.

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Notes

  1. 1.

    And indeed, the clearest difference between ‘anaphora resolution’ as specified in theoretical linguistics/psycholinguistics and ‘coreference resolution’ as specified by the muc annotation guidelines lies precisely in the treatment of predicative and quantificational nps. In anaphora resolution predicative nps have no antecedents, but bound pronouns do; conversely, in coreference resolution the links between bound pronouns and the quantifiers that bound them are not annotated, but those between predicative nps and their ‘antecedents’ are. (See chapter “Annotated Corpora and Annotation Tools”.)

  2. 2.

    http://www.sfs.uni-tuebingen.de/en/ascl/resources/corpora/tueba-dz.html

  3. 3.

    https://code.google.com/p/nada-nonref-pronoun-detector/

  4. 4.

    GuiTAR’s dd resolution module is an implementation of the Vieira/Poesio algorithm, see chapter “Early Approaches to Anaphora Resolution: Theoretically Inspired and Heuristic-Based”.

  5. 5.

    In the case where no antecedent has been proposed, these features assume a value of − 1.

  6. 6.

    In the case where no antecedent has been proposed, these features assume a value of − 1.

  7. 7.

    However, note that a ceiling of 100 % may be a bit too high based on earlier work by Poesio and Vieira [49] where inter-annotator agreement on the task was estimated at K = 0. 76 (kappa value).

  8. 8.

    The baselines and ceiling scores were computed with some additional assumptions such as considering discourse-new those definite descriptions for which no antecedent was proposed by the original resolution algorithm.

  9. 9.

    The remaining 7 % were set aside for validation and parameter tuning.

  10. 10.

    The baseline here is baseline1 from the 10-X-Validation experiment (i.e., Vieira and Poesio’s direct anaphora algorithm).

  11. 11.

    Assuming perfect dn classification.

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Acknowledgements

This work was supported in part by the limosine project (Uryupina, Poesio), in part by the sensei project (Kabadjov, Poesio).

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Authors and Affiliations

  1. DISI University of Trento, Trento, Italy

    Olga Uryupina

  2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Mijail Kabadjov & Massimo Poesio

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  1. Olga Uryupina
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  2. Mijail Kabadjov
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  3. Massimo Poesio
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Correspondence to Olga Uryupina .

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Editors and Affiliations

  1. Trento, Italy

    Massimo Poesio

  2. Frankfurt am Main, Germany

    Roland Stuckardt

  3. Heidelberg, Germany

    Yannick Versley

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Uryupina, O., Kabadjov, M., Poesio, M. (2016). Detecting Non-reference and Non-anaphoricity. In: Poesio, M., Stuckardt, R., Versley, Y. (eds) Anaphora Resolution. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47909-4_13

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