Referential Choice
- 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 textWe 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.…
Verfasserangaben: | Andrej A. Kibrik, Mariya V. Khudyakova, Grigory B. Dobrov, Anastasia LinnikORCiD, Dmitrij A. Zalmanov |
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DOI: | https://doi.org/10.3389/fpsyg.2016.01429 |
ISSN: | 1664-1078 |
Titel des übergeordneten Werks (Englisch): | Frontiers in psychology |
Untertitel (Englisch): | Predictability and Its Limits |
Verlag: | Frontiers Research Foundation |
Verlagsort: | Lausanne |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 23.11.2016 |
Erscheinungsjahr: | 2016 |
Veröffentlichende Institution: | Universität Potsdam |
Datum der Freischaltung: | 01.12.2016 |
Freies Schlagwort / Tag: | cross-methodological approach; discourse production; machine learning; non-categoricity; referential choice |
Band: | 7 |
Fördernde Institution: | Universität Potsdam, Publikationsfonds |
Fördernummer: | PA 2016_36 |
Organisationseinheiten: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Linguistik |
DDC-Klassifikation: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Peer Review: | Referiert |
Fördermittelquelle: | Publikationsfonds der Universität Potsdam |
Publikationsweg: | Open Access |
Lizenz (Deutsch): | CC-BY - Namensnennung 4.0 International |
Externe Anmerkung: | Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Humanwissenschaftliche Reihe ; 306 |