Automatic dialogue act annotation within Arabic debates

Abstract

Dialogue acts play an important role in the identification of argumentative discourse structure in human conversations. In this paper, we propose an automatic dialogue acts annotation method based on supervised learning techniques for Arabic debates programs. The choice of this kind of corpora is justified by its large content of argumentative information. To experiment annotation results, we used a specific annotation scheme relatively reliable for our task with a kappa agreement of 84%. The annotation process was yield using Weka platform algorithms experimenting Naive Bayes, SVM and Decision Trees classifiers. We obtained encouraging results with an average accuracy of 53%.