Affiliations: Computational Sciences Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla, México
Correspondence:
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Corresponding author. Aurelio López-López, Computational Sciences Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, C.P. 72840, Tonantzintla, Puebla,
México. E-mail: [email protected].
Abstract: Academic writing is a complex task which 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 difficult to master. Aiming to contribute to develop this skill, we present a freely available annotated corpus to support research in argumentation in Spanish. To build it, we elaborated an annotation guide to identify argumentation in paragraphs. The guide also specified how to determine segments of sentences as a claim or premise, and to indicate relations (support or attack) between such segments. Then, an annotated corpus of 300 sections was created. After its construction, the corpus was used to perform an exploratory analysis which aimed to identify and present the amount of argumentation in each section, as well as resulting patterns for argument identification. Hence, we also report an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. The results of the experiments to evaluate argumentative paragraph detection were encouraging. In addition, we discuss a web-based prototype for argument detection in paragraphs to reach the broader academic community of students, instructors and researchers.
