TY  - JOUR
A1  - Kibrik, Andrej A.
A1  - Khudyakova, Mariya V.
A1  - Dobrov, Grigory B.
A1  - Linnik, Anastasia
A1  - Zalmanov, Dmitrij A.
T1  - Referential Choice: Predictability and Its Limits
JF  - Frontiers in psychology
N2  - 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.
KW  - referential choice
KW  - non-categoricity
KW  - machine learning
KW  - cross-methodological approach
KW  - discourse production
Y1  - 2016
U6  - https://doi.org/10.3389/fpsyg.2016.01429
SN  - 1664-1078
VL  - 7
SP  - 9939
EP  - 9947
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Kibrik, Andrej A.
A1  - Khudyakova, Mariya V.
A1  - Dobrov, Grigory B.
A1  - Linnik, Anastasia
A1  - Zalmanov, Dmitrij A.
T1  - Referential Choice
BT  - Predictability and Its Limits
JF  - Frontiers in psychology
N2  - 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.
KW  - referential choice
KW  - non-categoricity
KW  - machine learning
KW  - cross-methodological approach
KW  - discourse production
Y1  - 2016
U6  - https://doi.org/10.3389/fpsyg.2016.01429
SN  - 1664-1078
VL  - 7
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - GEN
A1  - Kibrik, Andrej A.
A1  - Khudyakova, Mariya V.
A1  - Dobrov, Grigory B.
A1  - Linnik, Anastasia
A1  - Zalmanov, Dmitrij A.
T1  - Referential Choice
BT  - Predictability and Its Limits
N2  - 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe - 306 
KW  - cross-methodological approach
KW  - discourse production
KW  - machine learning
KW  - non-categoricity
KW  - referential choice
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-100313
ER  -