A structure of persuasion in Galatians: epistolary and rhetorical appeal in an aural setting

The purpose of this paper is to ponder the reception of the Letter to the Galatians in an aural setting. How did the first recipients react, what can we expect that they remembered after having listened to the letter? Are there structural elements in the letter that would have aided the aural reception of the letter? In four readings, the investigation traces textual indicators of interaction and emotion, compares their locations with epistolary and rhetorical structure-analysis and identifies a structure of persuasion. The focus on listeners is motivated by the supposition that illiteracy was the rule rather than the exception among those to whom the letter to the Galatians was sent. The different readings reveal a structure of persuasion with a realistic prospect to succeed as a mnemonic device in an aural setting on a macro-structural level. Situational passages (1:6-10; 3:1-5; 4:8-20; 5:2-12 and 6:12-13), together with recurring affirmations of Christ and Paul as embodiments of faithfulness and commitment in suffering, imprint on the aural memory of the first listeners a concern for an imitatio Christi crucifixi

Synthesizing Argumentation Frameworks from Examples

Peer reviewe

Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation

In the constellations approach to probabilistic argumentation, there is a probability distribution over the subgraphs of an argument graph, and this can be used to represent the uncertainty in the structure of the argument graph. In this paper, we consider how we can construct this probability distribution from data. We provide a language for data based on perspectives (opinions) on the structure of the graph, and we introduce a framework (based on general properties and some specific proposals) for aggregating these perspectives, and as a result obtaining a probability distribution that best reflects these perspectives. This can be used in applications such as summarizing collections of online reviews and combining conflicting reports

Argumentative Writing Support by means of Natural Language Processing

Persuasive essay writing is a powerful pedagogical tool for teaching argumentation skills. So far, the provision of feedback about argumentation has been considered a manual task since automated writing evaluation systems are not yet capable of analyzing written arguments. Computational argumentation, a recent research field in natural language processing, has the potential to bridge this gap and to enable novel argumentative writing support systems that automatically provide feedback about the merits and defects of written arguments. The automatic analysis of natural language arguments is, however, subject to several challenges. First of all, creating annotated corpora is a major impediment for novel tasks in natural language processing. At the beginning of this research, it has been mostly unknown whether humans agree on the identification of argumentation structures and the assessment of arguments in persuasive essays. Second, the automatic identification of argumentation structures involves several interdependent and challenging subtasks. Therefore, considering each task independently is not sufficient for identifying consistent argumentation structures. Third, ordinary arguments are rarely based on logical inference rules and are hardly ever in a standardized form which poses additional challenges to human annotators and computational methods. To approach these challenges, we start by investigating existing argumentation theories and compare their suitability for argumentative writing support. We derive an annotation scheme that models arguments as tree structures. For the first time, we investigate whether human annotators agree on the identification of argumentation structures in persuasive essays. We show that human annotators can reliably apply our annotation scheme to persuasive essays with substantial agreement. As a result of this annotation study, we introduce a unique corpus annotated with fine-grained argumentation structures at the discourse-level. Moreover, we pre- sent a novel end-to-end approach for parsing argumentation structures. We identify the boundaries of argument components using sequence labeling at the token level and propose a novel joint model that globally optimizes argument component types and argumentative relations for identifying consistent argumentation structures. We show that our model considerably improves the performance of local base classifiers and significantly outperforms challenging heuristic baselines. In addition, we introduce two approaches for assessing the quality of natural language arguments. First, we introduce an approach for identifying myside biases which is a well-known tendency to ignore opposing arguments when formulating arguments. Our experimental results show that myside biases can be recognized with promising accuracy using a combination of lexical features, syntactic features and features based on adversative transitional phrases. Second, we investigate for the first time the characteristics of insufficiently supported arguments. We show that insufficiently supported arguments frequently exhibit specific lexical indicators. Moreover, our experimental results indicate that convolutional neural networks significantly outperform several challenging baselines

Probabilistic Argumentation for Patient Decision Making

Medical drug reviews are increasingly commonplace on the web and have become an important source of information for patients undergoing medical treatment. Patients will look to these reviews in order to understand the impact the drugs have had on others who have experienced them. In short these reviews can be interpreted as a body of arguments and counterarguments for/against the drug being reviewed. One of the challenges of reading these reviews is drawing out the arguments easily and forming a final opinion; this is due to the number of reviews and the variety of arguments presented. This thesis explores the use of computational models of argumentation in order to extract structured argumentation data from the reviews and present them to the user. In particular I propose a pipeline that performs argument extraction, argument graph extraction and visualisation