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Kibrik AA, Khudyakova MV, Dobrov GB, et al. Referential Choice: Predictability and Its Limits. Front Psychol. 2016;7:1429doi: 10.3389/fpsyg.2016.01429.
Kibrik, A. A., Khudyakova, M. V., Dobrov, G. B., Linnik, A., & Zalmanov, D. A. (2016). Referential Choice: Predictability and Its Limits. Frontiers in psychology, 71429. https://doi.org/10.3389/fpsyg.2016.01429
Kibrik, Andrej A, et al. "Referential Choice: Predictability and Its Limits." Frontiers in psychology vol. 7 (2016): 1429. doi: https://doi.org/10.3389/fpsyg.2016.01429
Kibrik AA, Khudyakova MV, Dobrov GB, Linnik A, Zalmanov DA. Referential Choice: Predictability and Its Limits. Front Psychol. 2016 Sep 23;7:1429. doi: 10.3389/fpsyg.2016.01429. eCollection 2016. PMID: 27721800; PMCID: PMC5033969.
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Front Psychol. 2016 Sep 23;7:1429. doi: 10.3389/fpsyg.2016.01429. eCollection 2016.

Referential Choice: Predictability and Its Limits.

Frontiers in psychology

Andrej A Kibrik, Mariya V Khudyakova, Grigory B Dobrov, Anastasia Linnik, Dmitrij A Zalmanov

Affiliations

  1. Department of Typology and Areal Linguistics, Institute of Linguistics, Russian Academy of SciencesMoscow, Russia; Department of Theoretical and Applied Linguistics, Lomonosov Moscow State UniversityMoscow, Russia.
  2. Neurolinguistics Laboratory, National Research University Higher School of Economics Moscow, Russia.
  3. Consultant Plus Moscow, Russia.
  4. Linguistics Department, University of Potsdam Potsdam, Germany.
  5. Department of Theoretical and Applied Linguistics, Lomonosov Moscow State University Moscow, Russia.

PMID: 27721800 PMCID: PMC5033969 DOI: 10.3389/fpsyg.2016.01429

Abstract

We report a study of referential choice in discourse production, understood as the choice between various types of referential devices, such as pronouns and full noun phrases. Our goal is to predict referential choice, and to explore to what extent such prediction is possible. Our approach to referential choice includes a cognitively informed theoretical component, corpus analysis, machine learning methods and experimentation with human participants. Machine learning algorithms make use of 25 factors, including referent's properties (such as animacy and protagonism), the distance between a referential expression and its antecedent, the antecedent's syntactic role, and so on. Having found the predictions of our algorithm to coincide with the original almost 90% of the time, we hypothesized that fully accurate prediction is not possible because, in many situations, more than one referential option is available. This hypothesis was supported by an experimental study, in which participants answered questions about either the original text in the corpus, or about a text modified in accordance with the algorithm's prediction. Proportions of correct answers to these questions, as well as participants' rating of the questions' difficulty, suggested that divergences between the algorithm's prediction and the original referential device in the corpus occur overwhelmingly in situations where the referential choice is not categorical.

Keywords: cross-methodological approach; discourse production; machine learning; non-categoricity; referential choice

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