Affiliations: [a] Computational Sciences Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla, México | [b] Department of Information Technologies and Communication, Technological University of Nogales, Sonora, México
Correspondence:
[*]
Corresponding author. Jesús Miguel García-Gorrostieta, Computational Sciences Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, México. Tel.: +52 2222663100; E-mail: [email protected].
Abstract: The argumentation in academic writings is necessary to clearly communicate the ideas of the students. The relations between argumentative components are an essential part since this shows the contrast or support of the presented ideas. In this paper, we present two approaches to relation identification between pairs of components. In the first, we detect initially which components are related, to later classify them in support or attack relation. In the second approach, we identify directly which components have a support relation. For these approaches, we employed machine learning techniques with representations of several lexical, syntactic, semantic, structural and indicator features. Experiments in argumentative sections of academic theses showed that the models achieve encouraging results solving the task, and revealing the argumentative structures prevailing in student writings.
