@article{KibrikKhudyakovaDobrovetal.2016,
  author    = {Kibrik, Andrej A. and Khudyakova, Mariya V. and Dobrov, Grigory B. and Linnik, Anastasia and Zalmanov, Dmitrij A.},
  title     = {Referential Choice: Predictability and Its Limits},
  series = {Frontiers in psychology},
  volume    = {7},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2016.01429},
  pages     = {9939 -- 9947},
  year      = {2016},
  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.},
  language  = {en}
}
@article{KibrikKhudyakovaDobrovetal.2016,
  author    = {Kibrik, Andrej A. and Khudyakova, Mariya V. and Dobrov, Grigory B. and Linnik, Anastasia and Zalmanov, Dmitrij A.},
  title     = {Referential Choice},
  series = {Frontiers in psychology},
  volume    = {7},
  journal   = {Frontiers in psychology},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2016.01429},
  year      = {2016},
  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.},
  language  = {en}
}
@article{KongLinnikLawetal.2017,
  author    = {Kong, Anthony Pak-Hin and Linnik, Anastasia and Law, Sam-Po and Shum, Waisa Wai-Man},
  title     = {Measuring discourse coherence in anomic aphasia using Rhetorical Structure Theory},
  series = {International Journal of Speech-Language Pathology},
  volume    = {20},
  journal   = {International Journal of Speech-Language Pathology},
  number    = {4},
  publisher = {Routledge},
  address   = {Abingdon},
  issn      = {1754-9507},
  doi       = {10.1080/17549507.2017.1293158},
  pages     = {406 -- 421},
  year      = {2017},
  abstract  = {Purpose: The existing body of work regarding discourse coherence in aphasia has provided mixed results, leaving the question of coherence being impaired or intact as a result of brain injury unanswered. In this study, discourse coherence in non-brain-damaged (NBD) speakers and speakers with anomic aphasia was investigated quantitatively and qualitatively. Method: Fifteen native speakers of Cantonese with anomic aphasia and 15 NBD participants produced 60 language samples. Elicitation tasks included story-telling induced by a picture series and a procedural description. The samples were annotated for discourse structure in the framework of Rhetorical Structure Theory (RST) in order to analyse a number of structural parameters. After that 20 naive listeners rated coherence of each sample. Result: Disordered discourse was rated as significantly less coherent. The NBD group demonstrated a higher production fluency than the participants with aphasia and used a richer set of semantic relations to create discourse, particularly in the description of settings, expression of causality, and extent of elaboration. People with aphasia also tended to omit essential information content. Conclusion: Reduced essential information content, lower degree of elaboration, and a larger amount of structural disruptions may have contributed to the reduced overall discourse coherence in speakers with anomic aphasia.},
  language  = {en}
}