Speech Processing in Essay-Argumentative Texts: An Approach Based on Argument Mining and Supervised Machine Learning
Abstract
Identifying the structure of the argument is useful to analyze student’s speech and logical reasoning. The discourse understanding process includes the understanding of morphology, syntax and semantics. In this work, speech processing follows a methodology composed by the following phases: (i) creation of a corpus for training and validation of learning; (ii) implementation of the argument mining approach; and (iii) application of machine learning techniques. The results achieved were among the average found in the literature for works carried out in the English language. The identification of the intervention proposal was the highlight, with an F-value of 0.744.
Keywords:
Structure of the argument, Discourse processing, Corpus, Machine Learning
References
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Barros, F. de A., & Robin, J. (1996). Processamento de Linguagem Natural (p. 61). Sociedade Brasileira de Computação.
Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2010). Linear Programming and Network Flows (4th ed.). Wiley.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Desilia, Y., Utami, V. T., Arta, C., & Suhartono, D. (2017). An attempt to combine features in classifying argument components in persuasive essays. Workshop on Computational Models of Natural Argument - CEUR, 18., 2048(July), 71–75.
Dikli, S. (2006). An Overview of Automated Scoring of Essays. Journal of Technology Learning and Assessment, 5(1).
Gema, A. P., Winton, S., David, T., Suhartono, D., Shodiq, M., & Gazali, W. (2017). It Takes Two to Tango: Modification of Siamese Long Short Term Memory Network with Attention Mechanism in Recognizing Argumentative Relations in Persuasive Essay. Procedia Computer Science, 116, 449–459. https://doi.org/10.1016/j.procs.2017.10.036
Haykin, S. (2001). Redes Neurais: Princípios e Prática (2nd ed.). Bookman.
INEP. (2017). Redação no ENEM 2017: Cartilha do Participante. http://download.inep.gov.br/educacao_basica/enem/guia_participante/2017/manual _de_redacao_do_enem_2017.pdf
Krippendorff, K. (2004). Measuring the Reliability of Qualitative Text Analysis Data. Quality & Quantity, 38(6), 787–800. https://doi.org/10.1007/s11135-004-8107-7
Kurdi, M. Z. (2016). Natural Language Processing and Computational Linguistics 1: Speech, Morphology, Syntax (1st ed.). ISTE - Wiley.
Lima, F., Haendchen Filho, A., Prado, H. Do, & Ferneda, E. (2018). Automatic Evaluation of Textual Cohesion in Essays. International Conference on Computational Linguistics and Intelligent Text - CICLing, 19.,.
Lorena, ana carolina, & Carvalho, A. c. p. l. f. (2007). Uma Introdução às Support Vector Machines. Revista de Informática Teórica e Aplicada, 14(2), 43–67.
Luger, G. F. (2013). Inteligência Artificial (6th ed.). Pearson Education do Brasil.
Nguyen, H., & Litman, D. (2016). Improving Argument Mining in Student Essays by Learning and Exploiting Argument Indicators versus Essay Topics. International Florida Artificial Intelligence Research Society Conference - FLAIRS, 29., 485– 490.
Passero, G. (2018). Detecção De Fuga Ao Tema Em Redações Na Língua Portuguesa. In Dissertação de Mestrado. Universidade do Vale do Itajaí.
Persing, I., & Ng, V. (2016). End-to-End Argumentation Mining in Student Essays. Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT, 1384– 1394. https://doi.org/10.18653/v1/n16-1164
Russell, S., & Norvig, P. (2004). Inteligência Artificial (2nd ed.). Elsevier.
Stab, C., & Gurevych, I. (2017). Parsing Argumentation Structures in Persuasive Essays. Computational Linguistics, 43(3), 619–659. https://doi.org/10.1162/COLI_a_00295
Stab, C., & Gurevych, I. (2014). Identifying Argumentative Discourse Structures in Persuasive Essays. Conference on Empirical Methods in Natural Language Processing - EMNLP, 46–56. https://doi.org/10.3115/v1/d14-1006
Stede, M., & Schneider, J. (2018). Argumentation Mining. Synthesis Lectures on Human Language Technologies, 11(2), 1–191. https://doi.org/10.2200/S00883ED1V01Y201811HLT040
Tahir, S. B. ud D. (2017). Comparative Analysis of Supervised Learning Approaches for Argument Identification. IEEE International Multi-Topic Conference - INMIC, 20., 20, 1–5.
Val, M. da G. C. (2009). Redação e Textualidade (3rd ed.). Martins Fontes.
Walton, D. (2009). Argumentation in Artificial Intelligence. In G. Simari & I. Rahwan (Eds.), Argumentation in Artificial Intelligence. Springer US. https://doi.org/10.1007/978-0-387-98197-0
Published
2020-06-30
How to Cite
NAU, Jonathan; DAZZI, Rudimar Luís Scaranto; HAENDCHEN FILHO, Aluizio; FERNANDES, Anita.
Speech Processing in Essay-Argumentative Texts: An Approach Based on Argument Mining and Supervised Machine Learning. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 47. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 48-59.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2020.11316.
