Using Argumentative Structure to Grade Persuasive Essays
Andreas Stiegelmayr, Margot Mieskes · Lecture notes in computer science · 2018
In this work we analyse a set of persuasive essays, which were marked and graded with respect to their overall quality. Additionally, we performed a small-scale machine learning experiment incorporating features from the argumentative analysis in order to automatically classify good and bad essays on a four-point scale. Our results indicate that bad essays suffer from more than just incomplete argument structures, which is already visible using simple surface features. We show that good essays distinguish themselves in terms of the amount of argumentative elements (such as major claims, premises, etc.) they use. The results, which have been obtained using a small corpus of essays in German, indicate that information about the argumentative structure of a text is helpful in distinguishing good and bad essays. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.