The Speaker’s Perspective

Hendriks, Petra

doi:10.1007/978-94-007-6901-4_4

Cited by 1 publication

(1 citation statement)

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“…Referential variation may be due to the interlocutors’ perspective taking and their efforts to coordinate cognitive processes, see e.g., Koolen et al (2011) , Heller et al (2012) , and Baumann et al (2014) . A number of corpus-based studies and psycholinguistic studies explored various factors involved in the phenomenon of overspecification, occurring regularly in natural language (e.g., Kaiser et al, 2011 ; Hendriks, 2014 ; Vogels et al, 2014 ; Fukumura and van Gompel, 2015 ). Kibrik (2011 , pp.…”

Section: Experimental Studies Of Referential Variationmentioning

confidence: 99%

Referential Choice: Predictability and Its Limits

Kibrik

Mariya

Dobrov³

et al. 2016

Front. Psychol.

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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.

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