TY - THES A1 - Linnik, Anastasia T1 - Coherence and structure in aphasic and non-aphasic spoken discourse T1 - Kohärenz und Struktur im aphasischen und nicht-aphasischen gesprochenen Diskurs N2 - 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. N2 - Die Diskursproduktion ist entscheidend für den kommunikativen Erfolg und ist ausschlaggebend für die Diagnostik und Behandlung von Aphasie. Durch Kohärenz wird eine Reihe von Äußerungen oder Sätzen zum Diskurs. Kohärenz stellt die innere Einheit und Verbundenheit eines Textes dar und ist damit vielleicht die zentrale Eigenschaft des Diskurses. Es ist unklar, ob Menschen mit Aphasie, die Schwierigkeiten bei der Sprachproduktion haben, die Fähigkeit bewahren, einen kohärenten Diskurs zu konstruieren. Die Frage, was genau Sprache kohärent macht, wurde in der Literatur behandelt, ist aber nicht vollständig verstanden. Diese Dissertation stellt eine Untersuchung der aphasischen Diskursproduktion und der linguistischen Mechanismen zur Herstellung von Kohärenz dar. KW - aphasia KW - discourse production KW - rhetorical structure theory KW - coherence KW - Aphasie KW - Diskursproduktion KW - rhetorische Struktur KW - Kohärenz Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-423202 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 - 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 -