Referential choice: Predictability and its limits
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 refere...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-09-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01429/full |
| _version_ | 1811199228801712128 |
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| author | Andrej A Kibrik Andrej A Kibrik Mariya V. Khudyakova Grigory B. Dobrov Anastasia Linnik Dmitrij A. Zalmanov |
| author_facet | Andrej A Kibrik Andrej A Kibrik Mariya V. Khudyakova Grigory B. Dobrov Anastasia Linnik Dmitrij A. Zalmanov |
| author_sort | Andrej A Kibrik |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T01:44:28Z |
| format | Article |
| id | doaj.art-f0748c65f16f46bfba29f7d96ccded5c |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-12T01:44:28Z |
| publishDate | 2016-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-f0748c65f16f46bfba29f7d96ccded5c2022-12-22T03:53:07ZengFrontiers Media S.A.Frontiers in Psychology1664-10782016-09-01710.3389/fpsyg.2016.01429169517Referential choice: Predictability and its limitsAndrej A Kibrik0Andrej A Kibrik1Mariya V. Khudyakova2Grigory B. Dobrov3Anastasia Linnik4Dmitrij A. Zalmanov5Russian Academy of SciencesMoscow State UniversityNational Research University Higher School of EconomicsConsultant PlusUniversity of PotsdamMoscow State UniversityWe 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.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01429/fullmachine learningReferential choiceDiscourse productionCross-methodological approachnon-categoricity |
| spellingShingle | Andrej A Kibrik Andrej A Kibrik Mariya V. Khudyakova Grigory B. Dobrov Anastasia Linnik Dmitrij A. Zalmanov Referential choice: Predictability and its limits Frontiers in Psychology machine learning Referential choice Discourse production Cross-methodological approach non-categoricity |
| title | Referential choice: Predictability and its limits |
| title_full | Referential choice: Predictability and its limits |
| title_fullStr | Referential choice: Predictability and its limits |
| title_full_unstemmed | Referential choice: Predictability and its limits |
| title_short | Referential choice: Predictability and its limits |
| title_sort | referential choice predictability and its limits |
| topic | machine learning Referential choice Discourse production Cross-methodological approach non-categoricity |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01429/full |
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