Identifying Argumentative Paragraphs: Towards Automatic Assessment of Argumentation in Theses

Abstract

The academic revisions by instructors is a critical step of the writing process, revealing deficiencies to the students, such as lack of argumentation. Argumentation is needed to communicate clearly ideas and to convince the reader of the stated claims. This paper presents three models to identify argumentative paragraphs in different sections (Problem Statement, Justification, and Conclusion) of theses and determine their level of argumentation. The task is achieved using machine learning techniques with lexical features. The models came from an annotated collection of student writings, which served for training. We performed experiments to evaluate argumentative paragraph identification in the sections, reaching encouraging results compared to previously proposed approaches. Several feature configurations and learning algorithms were tested to reach the models. We applied the models in a web-based argument assessment system to provide access to students, and instructors.