Abstract
Argumentation in student research writings is needed to clearly communicate ideas and convince the reader of the presented claims. In this paper, we introduce a methodology to approach the analysis of argumentative writing in undergraduate research texts. We elaborate an annotation scheme to detect claims/premises and support/attack relations. An exploratory analysis was carried out to know the amount of argumentation in selected sections of theses. We analyze five types of argumentation (Authority, Example, Causal, Comparison and Analogy) in these sections. And we also explore the identification of arguments in paragraphs using machine learning techniques with lexical features, with encouraging results.
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Acknowledgments
We thank the annotator Tania Maria Tequida Castillo for the assistance in the corpus creation. The first author was partially supported by CONACYT, México, under scholarship 357381. The second author was partially supported by SNI, México.
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Gorrostieta, J.M.G., López-López, A. (2016). Argumentation Identification for Academic Support in Undergraduate Writings. In: Verbert, K., Sharples, M., Klobučar, T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science(), vol 9891. Springer, Cham. https://doi.org/10.1007/978-3-319-45153-4_8
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DOI: https://doi.org/10.1007/978-3-319-45153-4_8
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