An Integrated Program for Teaching Writing and Thinking Skills

The nature and development of thought, the connection between thinking and writing, teaching practices associated with the direct teaching of thinking and writing skills were researched and studied. An integrated program of writing and thinking skills was compiled. This program includes techniques for teaching thinking and writing activities and provides step by step procedures to accomplish the integration of thinking skills with writing skills targeted at producing contrast and comparison essays. A discussion and recommendations regarding the program are included

05.11: Information Structures

Upon completion of this chapter, readers will be able to: Define information structure. Explain the contents and organization of different types of information structures

Automatic extraction and structure of arguments in legal documents

A argumentação desempenha um papel fundamental na comunicação humana ao formular razões e tirar conclusões. Desenvolveu-se um sistema automático para identificar argumentos jurídicos de forma eficaz em termos de custos a partir da jurisprudência. Usando 42 leis jurídicas do Tribunal Europeu dos Direitos Humanos (ECHR), anotou-se os documentos para estabelecer um conjunto de dados “padrão-ouro”. Foi então desenvolvido e testado um processo composto por 3 etapas para mineração de argumentos. A primeira etapa foi avaliar o melhor conjunto de recursos para identificar automaticamente as frases argumentativas do texto não estruturado. Várias experiencias foram conduzidas dependendo do tipo de características disponíveis no corpus, a fim de determinar qual abordagem que produzia os melhores resultados. No segundo estágio, introduziu-se uma nova abordagem de agrupamento automático (para agrupar frases num argumento legal coerente), através da utilização de dois novos algoritmos: o “Algoritmo de Identificação do Grupo Apropriado”, ACIA e a “Distribuição de orações no agrupamento de Cluster”, DSCA. O trabalho inclui também um sistema de avaliação do algoritmo de agrupamento que permite ajustar o seu desempenho. Na terceira etapa do trabalho, utilizou-se uma abordagem híbrida de técnicas estatísticas e baseadas em regras para categorizar as orações argumentativas. No geral, observa-se que o nível de precisão e utilidade alcançado por essas novas técnicas é viável como base para uma estrutura geral de argumentação e mineração; Abstract: Automatic Extraction and Structure of Arguments in Legal Documents Argumentation plays a cardinal role in human communication when formulating reasons and drawing conclusions. A system to automatically identify legal arguments cost-effectively from case-law was developed. Using 42 legal case-laws from the European Court of Human Rights (ECHR), an annotation was performed to establish a ‘gold-standard’ dataset. Then a three-stage process for argument mining was developed and tested. The first stage aims at evaluating the best set of features for automatically identifying argumentative sentences within unstructured text. Several experiments were conducted, depending upon the type of features available in the corpus, in order to determine which approach yielded the best result. In the second stage, a novel approach to clustering (for grouping sentences automatically into a coherent legal argument) was introduced through the development of two new algorithms: the “Appropriate Cluster Identification Algorithm”,(ACIA) and the “Distribution of Sentence to the Cluster Algorithm” (DSCA). This work also includes a new evaluation system for the clustering algorithm, which helps tuning it for performance. In the third stage, a hybrid approach of statistical and rule-based techniques was used in order to categorize argumentative sentences. Overall, it’s possible to observe that the level of accuracy and usefulness achieve by these new techniques makes it viable as the basis of a general argument-mining framework

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

34. Information Structures

The main parts of a technical-writing course focus on applications—ways technical writing skills are applied in the real world. However, these applications use varying combinations of information infrastructures. An information infrastructure is (1) a type of information content (such as descriptive writing), (2) a way of organizing information (such as a comparison or classification), or (3) both. The information infrastructures reviewed in this appendix are the ones commonly used in technical writing. Of course, there are other infrastructures—maybe some that scholars of technical writing have not yet pinned a label on, but these are the most common and the most readily visible. And of course some of these infrastructures blend together. The main thing is that by knowing these, you have the intellectual tools for quickly organizing and structuring just about any writing project

ChangeMyView Through Concessions: Do Concessions Increase Persuasion?

In Discourse Studies concessions are considered among those argumentative strategies that increase persuasion. We aim to empirically test this hypothesis by calculating the distribution of argumentative concessions in persuasive vs. non-persuasive comments from the the ChangeMyView subreddit. This constitutes a challenging task since concessions do not always bear an argumentative role and are expressed through polysemous lexical markers. Drawing from a theoretically-informed typology of concessions, we first conduct a crowdsourcing task to label a set of polysemous lexical markers as introducing an argumentative concession relation or not. Second, we present a self-training method to automatically identify argumentative concessions using linguistically motivated features. While we achieve a moderate F1 of 57.4% via the self-training method, our subsequent error analysis highlights that the self training method is able to generalize and identify other types of concessions that are argumentative, but were not considered in the annotation guidelines. Our findings from the manual labeling and the classification experiments indicate that the type of argumentative concessions we investigated is almost equally likely to be used in winning and losing arguments. While this result seems to contradict theoretical assumptions, we provide some reasons related to the ChangeMyView subreddit

Argumentation models and their use in corpus annotation: practice, prospects, and challenges

The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora