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This thesis aims to investigate the visualization approaches in the field of annotated discourse relations and to find a solution that meets the requirements best by comparing different programming tools. The subject of this research are coherence relations, which have several properties that can be challenging for many visualization methods. The thesis presents five different visualization options from both the application and the development perspective. The initially tested simple HTML approaches as well as the software package displaCy show the insufficient level for the visualization purposes of this work. The alternative implementation with D3 would optimally meet the requirements but goes beyond the scope of the project. The main method chosen in this thesis was implemented as a single web application and uses the brat annotation tool, which fulfills most of the defined requirements for the representation of the coherence relations. The application graphically displays the coherence relations annotated in the text and offers a filter function for different relation types.
We present novel experimental evidence on the availability and the status of exhaustivity inferences with focus partitioning in German, English, and Hungarian. Results suggest that German and English focus-background clefts and Hungarian focus share important properties, (É. Kiss 1998, 1999; Szabolcsi 1994; Percus 1997; Onea & Beaver 2009). Those constructions are anaphoric devices triggering an existence presupposition. EXH-inferences are not obligatory in such constructions in English, German, or Hungarian, against some previous literature (Percus 1997; Büring & Križ 2013; É. Kiss 1998), but in line with pragmatic analyses of EXH-inferences in clefts (Horn 1981, 2016; Pollard & Yasavul 2016). The cross-linguistic differences in the distribution of EXH-inferences are attributed to properties of the Hungarian number marking system.
We present novel experimental evidence on the availability and the status of exhaustivity inferences with focus partitioning in German, English, and Hungarian. Results suggest that German and English focus-background clefts and Hungarian focus share important properties, (É. Kiss 1998, 1999; Szabolcsi 1994; Percus 1997; Onea & Beaver 2009). Those constructions are anaphoric devices triggering an existence presupposition. EXH-inferences are not obligatory in such constructions in English, German, or Hungarian, against some previous literature (Percus 1997; Büring & Križ 2013; É. Kiss 1998), but in line with pragmatic analyses of EXH-inferences in clefts (Horn 1981, 2016; Pollard & Yasavul 2016). The cross-linguistic differences in the distribution of EXH-inferences are attributed to properties of the Hungarian number marking system.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
In this paper, we address some controversially debated empirical questions concerning object fronting in German by a series of acceptability rating studies. We investigated three kinds of factors: (i) properties of the subject (given/new, pronoun/full DP), (ii) emphasis, (iii) register. The first factor is predicted to play a crucial role by models in which object fronting possibilities are limited by prosodic properties. Two experiments provide converging evidence for a systematic effect of this factor: we find that the relative acceptability of object fronting across subjects that require an accent (new DPs) is lower than across deaccentable subjects (pronouns and given DPs). Other models predict object fronting across full phrases (but not across pronouns) to be limited to an emphatic interpretation. This prediction is also borne out, suggesting that both types of models capture an empirically valid generalization and can be seen as complementing each other rather than competing with each other. Finally, we find support for the view that informal register facilitates object fronting. In sum, our experiments contribute to clarifying the empirical basis concerning a phenomenon influenced by a range of interacting factors. This, in turn, informs theoretical approaches to the prefield position and helps to identify factors that need to be carefully controlled in this field of research.
Gender stereotypes influence subjective beliefs about the world, and this is reflected in our use of language. But do gender biases in language transparently reflect subjective beliefs? Or is the process of translating thought to language itself biased? During the 2016 United States (N = 24,863) and 2017 United Kingdom (N = 2,609) electoral campaigns, we compared participants' beliefs about the gender of the next head of government with their use and interpretation of pronouns referring to the next head of government. In the United States, even when the female candidate was expected to win, she pronouns were rarely produced and induced substantial comprehension disruption. In the United Kingdom, where the incumbent female candidate was heavily favored, she pronouns were preferred in production but yielded no comprehension advantage. These and other findings suggest that the language system itself is a source of implicit biases above and beyond previously known biases, such as those measured by the Implicit Association Test.
Gender stereotypes influence subjective beliefs about the world, and this is reflected in our use of language. But do gender biases in language transparently reflect subjective beliefs? Or is the process of translating thought to language itself biased? During the 2016 United States (N = 24,863) and 2017 United Kingdom (N = 2,609) electoral campaigns, we compared participants' beliefs about the gender of the next head of government with their use and interpretation of pronouns referring to the next head of government. In the United States, even when the female candidate was expected to win, she pronouns were rarely produced and induced substantial comprehension disruption. In the United Kingdom, where the incumbent female candidate was heavily favored, she pronouns were preferred in production but yielded no comprehension advantage. These and other findings suggest that the language system itself is a source of implicit biases above and beyond previously known biases, such as those measured by the Implicit Association Test.
A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jager et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005. <br /> Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model. <br /> The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified.