@misc{LinnikBastiaanseHoehle2016,
  author    = {Linnik, Anastasia and Bastiaanse, Roelien and H{\"o}hle, Barbara},
  title     = {Discourse production in aphasia: a current review of theoretical and methodological challenges},
  series = {Aphasiology : an international, interdisciplinary journal},
  volume    = {30},
  journal   = {Aphasiology : an international, interdisciplinary journal},
  publisher = {Copernicus},
  address   = {Abingdon},
  issn      = {0268-7038},
  doi       = {10.1080/02687038.2015.1113489},
  pages     = {765 -- 800},
  year      = {2016},
  abstract  = {Background: Discourse abilities play an important role in the assessment, classification, and therapy outcome evaluation of people with aphasia. Discourse production in aphasia has been studied quite extensively in the last 15 years. Nevertheless, many questions still do not have definitive answers.Aims: The aim of this review is to present the current situation in the research on a number of crucial aspects of discourse production in aphasia, focusing on methodological progress and related challenges. This review continues the discussion of the core themes in the field, aiming to render it as up-to-date as possible.Main Contribution: The review focuses on a number of unexplored theoretical issues, specifically, the interface between micro- and macrolinguistic abilities, and the relationship between linguistic competence and communicative success in aphasia. The emphasis on theoretical challenges, along with the thorough discussion of methodological problems in the field, makes this review a starting point and a comprehensive information source for researchers planning to address language production in people with aphasia.Conclusion: Although the picture is not yet complete, recent advancements lead to a better understanding of the processes involved in aphasic discourse production. Different approaches provide insights into the complex multifaceted nature of discourse-level phenomena; however, methodological issues, including low comparability, substantially slow down the progress in the field.},
  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: 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}
}
@phdthesis{Linnik2016,
  author    = {Linnik, Anastasia},
  title     = {Coherence and structure in aphasic and non-aphasic spoken discourse},
  doi       = {10.25932/publishup-42320},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423202},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 106},
  year      = {2016},
  abstract  = {Discourse production is crucial for communicative success and is in the core of aphasia assessment and treatment. Coherence differentiates discourse from a series of utterances/sentences; it is internal unity and connectedness, and, as such, perhaps the most inherent property of discourse. It is unclear whether people with aphasia, who experience various language production difficulties, preserve the ability to produce coherent discourse. A more general question of how coherence is established and represented linguistically has been addressed in the literature, yet remains unanswered. This dissertation presents an investigation of discourse production in aphasia and the linguistic mechanisms of establishing coherence.},
  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}
}
@misc{KibrikKhudyakovaDobrovetal.2016,
  author    = {Kibrik, Andrej A. and Khudyakova, Mariya V. and Dobrov, Grigory B. and Linnik, Anastasia and Zalmanov, Dmitrij A.},
  title     = {Referential Choice},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-100313},
  pages     = {21},
  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}
}