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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. [ABSTRACT FROM AUTHOR]

Academic writing is a complex task that requires an author to be skilled in argumentation. The goal of an academic author is to communicate ideas clearly and to convince the reader of the veracity of their claims. However, few students in lower level courses are good arguers, as this is a skill that takes time to master. With the aim of contributing to the development of this skill, we introduce a freely available annotated corpus to support research on argumentation in Spanish. To build the corpus, we developed an annotation guide to identify argumentation in paragraphs. The guide also outlines how to annotate segments of sentences as either claims or premises and how to indicate relations (support or attack) between such components. Then, an annotated corpus of 444 sections was created, and an almost perfect interannotator agreement rate was achieved for a number of its components. After its construction, the corpus was used to perform an exploratory analysis, which aimed to identify the amount of argumentation in each section as well as the patterns commonly used for argument identification. Thereafter, experiments were conducted to automatically classify argumentative components using lexical, semantic, discourse, structural, and sentiment features. A Document Occurrence Representation was also tested on the corpus constituting a novel approach in the representation of documents for automatic argument detection. The results of these experiments to automatically classify argumentative components in Spanish are very promising and seem to support the idea that linguistic feature engineering plays a crucial role in text classification. [ABSTRACT FROM AUTHOR]