Affiliations: [a] Universidad de la Sierra, Moctezuma, Sonora 84560 México | [b] Department of Computational Scs., Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro. No. 1, Tonantzintla, Pue. 72840 México | [c] Department of Computer Scs., Universidad Tecnológica de Nogales, Av. Universidad 271, Nogales, Sonora 84097 México | [d] Department of Computer Scs., Centro de Investigación en Matemáticas, Jalisco S/N, Gto. Guanajuato 36023 México
Correspondence:
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Corresponding author. Aurelio López-López, Department of Computational Scs., Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro. No. 1, Tonantzintla, Pue. 72840 México. E-mail: [email protected].
Abstract: Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches.
Keywords: Academic writing, argumentation analysis, machine learning, text representation, natural language processing
