Skip to main content
SpringerLink
Log in
Book cover

The International Conference on Advances in Emerging Trends and Technologies

ICAETT 2019: Advances in Emerging Trends and Technologies pp 442–453Cite as

ANN-Based Model for Simple Grammatical Cases Teaching in Spanish Language

  • Conference paper
  • First Online:

  • 799 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1066))

Abstract

The Multilayer Perceptron (MLP) is one of the most powerful and popular network architectures, due to its ability to solve a large number of problems successfully. The objective of this work is to develop a model based on neural networks to solve simple grammar cases in the Spanish language, which in turn, allows the teaching of Artificial Neural Networks (ANN) in students. The presented model consists of 12 stages that allow to simulate simple grammatical cases. An illustrative example is presented with two programs that allow to simulate the grammatical case “Uppercase Identification” and “Infinitive Verbs” using MLP; each had a training scenario and a learning verification scenario.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. De Armas, T.: Redes Neuronales Artificiales. Fundamentos básicos de inteligencia artificial con Matlab, pp. 152–156. Educosta (2009)

    Google Scholar 

  2. Díaz, P.-C.J., Soria, O.E., Martínez, M.J., Escandell, M.P., Gómez, S.J.: Software educativo basado en las GUI de Matlab para cursos de redes neuronales. In: Psicología y salud mental: conceptos, metodologías, herramientas y aplicaciones, pp. 1730–1755. En I. Management Association (2016). http://doi.org/10.4018/978-1-5225-0159-6.ch075

  3. Ugur, A., Kinaci, A.-C.A.: A web-based tool for teaching neural network concepts. Comput. Appl. Eng. Educ. 18(3), 449–457 (2010)

    Article  Google Scholar 

  4. Matich, D.J.: Neural Networks: Basic Concepts and Applications. Universidad Tecnológica Nacional, México (2001)

    Google Scholar 

  5. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  6. Klein, M., Kamp, H., Palm, G., Doya, K.: A computational neural model of goal-directed utterance selection. Neural Netw. 23(5), 592–606 (2010)

    Article  Google Scholar 

  7. Carneiro, H.C., França, F.M., Lima, P.M.: Multilingual part-of-speech tagging with weightless neural networks. Neural Netw. 66, 11–21 (2015)

    Article  Google Scholar 

  8. Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)

    Google Scholar 

  9. Clarke, D.: A context-theoretic framework for compositionality in distributional semantics. Comput. Linguist. 38(1), 41–71 (2012)

    Article  Google Scholar 

  10. Nielsen, M.A.: Neural Networks and Deep Learning, vol. 25. Determination Press, USA (2015)

    Google Scholar 

  11. Haykin, S.S.: Rosenblatt’s perceptron. In: Neural Networks and Learning Machines, pp. 48–62 (2009)

    Google Scholar 

  12. Barandela, R., Gasca, E., Alejo, R.: Corrección de la Muestra para el Aprendizaje del Perceptron Multicapa. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial 5(13), 2–9 (2001)

    Google Scholar 

  13. Universidad de Córdoba. https://www.unicordoba.edu.co/index.php/facultad-educacion-y-ciencias-humanas/lic-informatica/plan-de-estudio-lic-informatica/

  14. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. Model. 5(3), 213–221 (1998)

    MATH  Google Scholar 

  15. García, N.B.: Implementación de técnicas de deep learning (2015). https://riull.ull.es/xmlui/handle/915/1409

  16. Longoni, M.G., Porcel, E., López, M.V., Dapozo, G.N.: Modelos de Redes Neuronales Perceptrón Multicapa y de Base Radial para la predicción del rendimiento académico de alumnos universitarios. In: XVI Congreso Argentino de Ciencias de la Computación (2010)

    Google Scholar 

  17. Alonso, A.-D.C., Jara, E.A.: Visión por computadora: identificación, clasificación y seguimiento de objetos. FPUNE Scientific, vol. 10 (2016)

    Google Scholar 

  18. Cinca, C.S., del Brío, B.M.: Predicción de la quiebra bancaria mediante el empleo de redes neuronales artificiales. Revista española de Financiación y Contabilidad. 74, 153–176 (1993)

    Google Scholar 

  19. Tabares, H., Branch, J., Valencia, J.: Generación dinámica de la topología de una red neuronal artificial del tipo Perceptron Multicapa. Revista Facultad de Ingeniería Universidad de Antioquia 38, 146–162 (2006)

    Google Scholar 

  20. Yiğit, T., Işik, A.H., Bilen, M.: Web based educational software for artificial neural networks. In: ICEMST 2014, pp. 629–632 (2014)

    Google Scholar 

  21. Zhang, Y., Sun, Y., Phillips, P., Liu, G., Zhou, X., Wang, S.: A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. J. Med. Syst. 40(7), 173 (2016)

    Article  Google Scholar 

  22. Parlos, A.G., Fernández, B., Atiya, A.F., Muthusami, J., Tsai, W.K.: Un algoritmo de aprendizaje acelerado para redes de perceptrón multicapa. IEEE Trans. Neural Netw. 5(3), 493–497 (1994)

    Article  Google Scholar 

  23. Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016)

    Article  MathSciNet  Google Scholar 

  24. Deperlioglu, O., Kose, U.: An educational tool for artificial neural networks. Comput. Electr. Eng. 37(3), 392–402 (2011)

    Article  Google Scholar 

  25. Díaz, M.P., Carrasco, J.J., Soria, O.E., Martínez M.J., Escandell M.P., Gómez S.J.: Educational software based on Matlab GUIs for neural networks courses. In: Handbook of Research on Computational Simulation and Modeling in Engineering, pp. 333–358. IGI Global (2016)

    Google Scholar 

  26. Aryadoust, V., Baghaei, P.: Does EFL readers’ lexical and grammatical knowledge predict their reading ability? Insights from a perceptron artificial neural network study. Educ. Assess. 21(2), 135–156 (2016)

    Article  Google Scholar 

  27. Gentleman, R., Ihaka, R.: R Project for Statistical Computing (3.6.0) (software) (1993). https://www.r-project.org/

  28. University of Waikato: WEKA (3.6.6) (software) (2006). https://www.cs.waikato.ac.nz/ml/index.html

  29. Eberhard Karls University of Tübingen.: SNNS (4.3) (software) (2008). http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

  30. Chartier, B.P., Mahler, K.: Machine Translation and Neural Networks for a multilingual EU. Zeitschrift für Europäische Rechtslinguistik (ZERL) (2018)

    Google Scholar 

  31. Vurkaç, M.: Prestructuring multilayer perceptrons based on information-theoretic modeling of a Partido-Alto-based Grammar for Afro-Brazilian music: enhanced generalization and principles of Parsimony, including an investigation of statistical paradigms. Portland State University (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Córdoba University, Montería, Colombia

    Laura Márquez García & Adán Gómez Salgado

Authors
  1. Laura Márquez García
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adán Gómez Salgado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laura Márquez García .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Joffre León-Acurio

  3. Universitat de Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

  4. Centro de Investigación y Desarrollo Profesional, Babahoyo, Los Rios Province, Ecuador

    Práxedes Montiel Díaz

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Márquez García, L., Salgado, A.G. (2020). ANN-Based Model for Simple Grammatical Cases Teaching in Spanish Language. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_41

Download citation

Publish with us

Policies and ethics

Search

Navigation