Advertisement

Answering Confucius: The Reason Why We Complicate

  • Bernardo Pereira Nunes
  • Stella Pedrosa
  • Ricardo Kawase
  • Mohammad Alrifai
  • Ivana Marenzi
  • Stefan Dietze
  • Marco Antonio Casanova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8095)

Abstract

Learning is a level-progressing process. In any field of study, one must master basic concepts to understand more complex ones. Thus, it is important that during the learning process learners are presented and challenged with knowledge which they are able to comprehend (not a level below, not a level too high). In this work we focus on language learners. By gradually improving (complicating) texts, readers are challenged to learn new vocabulary. To achieve such goals, in this paper we propose and evaluate the ‘complicator’ that translates given sentences to a chosen level of higher degree of difficulty. The ‘complicator’ is based on natural language processing and information retrieval approaches that perform lexical replacements. 30 native English speakers participated in a user study evaluating our methods on an expert-tailored dataset of children books. Results show that our tool can be of great utility for language learners who are willing to improve their vocabulary.

Keywords

Technology enhanced learning language development learning process 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arfé, B., Jane, O., Pianta, E., Alrifai, M.: Story simplification: User guide. Technical report d2.2, terence project, 2011, Technical report (2011), http://terenceproject.eu
  2. 2.
    da Graa Krieger, M.: Dicionários para o ensino de lngua materna: princpios e critérios de escolha. Revista Língua e Literatura Frederico Westphalen 6 e 7, 101–112 (2004/2005)Google Scholar
  3. 3.
    Navigli, R., Ponzetto, S.P.: Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence 193, 217–250 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Nunes, B.P., Kawase, R., Siehndel, P., Casanova, M.A., Dietze, S.: As simple as it gets - a sentence simplifier for different learning levels and contexts. In: ICALT (to appear, 2013)Google Scholar
  5. 5.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics, Stroudsburg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bernardo Pereira Nunes
    • 1
    • 2
    • 3
  • Stella Pedrosa
    • 3
  • Ricardo Kawase
    • 2
  • Mohammad Alrifai
    • 2
  • Ivana Marenzi
    • 2
  • Stefan Dietze
    • 2
  • Marco Antonio Casanova
    • 1
  1. 1.Department of InformaticsPUC-RioRio de JaneiroBrazil
  2. 2.L3S Research CenterLeibniz University HannoverGermany
  3. 3.Central Coordination for Distance LearningPUC-RioRio de JaneiroBrazil

Personalised recommendations