Mapeamento de Problemas Trigonométricos usando Deep Learning
Resumo
Este artigo apresenta uma abordagem baseada em Deep Learning para mapear automaticamente problemas trigonométricos em modelos de equações, como parte de um projeto maior de um Sistema Tutor Inteligente na área da Trigonometria. Esta abordagem traduz diretamente problemas matemáticos em modelos de equações usando um modelo de Rede Neural Recorrente (RNR), combinando um modelo baseado em conhecimento linguístico para tratar o contexto trigonométrico. Foram conduzidos experimentos usando Chatbot para a interação com os alunos.
Palavras-chave:
aprendizado profundo, problemas trigonométricos, modelos de equações, rede neural recorrente (RNN), sistema tutor inteligente
Referências
Gliozzo, A. et al. Building Cognitive Applications with IBM Watson Services. IBM Redbooks. 2017. 132 p.
Graesser, A. C., VanLehn, K., Rosé, C. P., Jordan, P. W. e Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4), 39-52.
Klaus, G. et al. (2016). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems.
Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O. e Ang, S. D. (2015). Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3, 585-597.
Kushman, N., Artzi, Y., Zettlemoyer, L. e Barzilay, R. (2014). Learning to automatically solve algebra word problems. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Vol. 1, p. 271-281.
LeCun, Y., Bengio, Y. e Hinton, G. E. (2015). Deep Learning. Nature, Vol. 521, p. 436-444.
Martins, F. J., Ferrari, D. N. e Geyer, C. F. R. (2003). jXChat - Um Sistema de Comunicação Eletrônica Inteligente para apoio a Educação a Distância. In SBIE - Brazilian Symposium on Computers in Education, p. 445-454.
Moraes, S. e Machado, R. (2016). Chatterbot for Education: a Study based on Formal Concept Analysis for Instructional Material Recommendation. In SBIE - Brazilian Symposium on Computers in Education, p. 1347-1351.
Roy, S. e Roth, D. (2016). Illinois Math Solver: Math Reasoning on the Web. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, p. 52-56.
Shi, S., Wang, Y., Lin, C. Y., Liu, X. e Rui, Y. (2015). Automatically solving number word problems by semantic parsing and reasoning. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, p. 1132-1142.
Sympy. Sympy Development Team. (2018). SymPy Documentation. Disponível em: [link]. Acesso em: 15 de junho de 2018.
Wang, Y., Liu, X. e Shi, S. (2017). Deep Neural Solver for Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, p. 845-854.
Wenger, E. (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. San Francisco: Morgan Kaufmann.
Graesser, A. C., VanLehn, K., Rosé, C. P., Jordan, P. W. e Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22(4), 39-52.
Klaus, G. et al. (2016). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems.
Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O. e Ang, S. D. (2015). Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3, 585-597.
Kushman, N., Artzi, Y., Zettlemoyer, L. e Barzilay, R. (2014). Learning to automatically solve algebra word problems. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Vol. 1, p. 271-281.
LeCun, Y., Bengio, Y. e Hinton, G. E. (2015). Deep Learning. Nature, Vol. 521, p. 436-444.
Martins, F. J., Ferrari, D. N. e Geyer, C. F. R. (2003). jXChat - Um Sistema de Comunicação Eletrônica Inteligente para apoio a Educação a Distância. In SBIE - Brazilian Symposium on Computers in Education, p. 445-454.
Moraes, S. e Machado, R. (2016). Chatterbot for Education: a Study based on Formal Concept Analysis for Instructional Material Recommendation. In SBIE - Brazilian Symposium on Computers in Education, p. 1347-1351.
Roy, S. e Roth, D. (2016). Illinois Math Solver: Math Reasoning on the Web. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, p. 52-56.
Shi, S., Wang, Y., Lin, C. Y., Liu, X. e Rui, Y. (2015). Automatically solving number word problems by semantic parsing and reasoning. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, p. 1132-1142.
Sympy. Sympy Development Team. (2018). SymPy Documentation. Disponível em: [link]. Acesso em: 15 de junho de 2018.
Wang, Y., Liu, X. e Shi, S. (2017). Deep Neural Solver for Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, p. 845-854.
Wenger, E. (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. San Francisco: Morgan Kaufmann.
Publicado
29/10/2018
Como Citar
KUYVEN, Neiva Larisane; VANZIN, Vinícius João de Barros; ANTUNES, Carlos André; CEMIN, Alexandra; SILVA, João Luis Tavares da; TAROUCO, Liane Margarida Rockenbach.
Mapeamento de Problemas Trigonométricos usando Deep Learning. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 29. , 2018, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 1513-1522.
DOI: https://doi.org/10.5753/cbie.sbie.2018.1513.
