skip to main content
10.1145/2428736.2428802acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiiwasConference Proceedingsconference-collections
research-article

A context-related vocabulary trainer in the integrated intelligent computer-assisted language learning (iiCALL) environment

Published:03 December 2012Publication History

ABSTRACT

The Integrated Intelligent Computer-Assisted Language Learning (iiCALL) environment is a Web-based e-learning platform that enables language learning in common working environments like Web browsers or email clients. Based on learning theories, it offers context-related learning scenarios for different learning types and different levels of learners. So far, the prototype implements the server architecture which runs inside an Apache Tomcat using Hibernate and MySQL for persistence purposes and which uses the General Architecture for Text Engineering (GATE) framework for Natural Language Processing (NLP) tasks. The prototype also implements a client as a Mozilla Firefox extension by using the XML User Interface Language (XUL) and JavaScript. It exemplarily shows a context-related vocabulary trainer, a learning scenario for language learners on a beginner's level. The paper explains technology of the prototype and points out open issues and future work.

References

  1. Amaral, L., Meurers, D. On Using Intelligent Computer-Assisted Language Learning in Real-Life Foreign Language Teaching and Learning. ReCALL 2011, Vol. 23, No 1, pp. 4--24, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Antoniadis, G., Echinard, S., Kraif, O., Lebarbé, T., Loiseau M. & Ponton, C. NLP-based scripting for CALL activities. In L. Lemnitzer, D. Meurers & E. Hinrichs (eds.), Proceedings of eLearning for Computational Linguistics and Computational Linguistics for eLearning, International Workshop in Association with COLING 2004, Geneva, Switzer-land: COLING, pp. 18--25, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Antoniadis, G., Granger, S., Kraif, O., Ponton, C. & Zampa, V. NLP and CALL: integration is working. In N. Kubler ed. Proceedings of TaLC7, 7th Conference of Teaching and Language Corpora. coll. Etudes contrastives. Bruxelles, Belgium, 2009.Google ScholarGoogle Scholar
  4. Baker, C. F., Filmore, C. J., Lowe, J. B. The Berkeley FrameNet Project. Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics -- Volume 1, pp. 86--90. Montreal, Quebec, Canada, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Boguraev, B. and Ando, R. K. TimeML-Compliant Text Analysis for Temporal Reasoning. Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 997--1003. Edinburgh, Scotland, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Boulton, A. Data-driven Learning: Reasonable Fears and Rational Reassurance. Indian Journal of Applied Linguistics 35(1), pp. 81--106, 2009.Google ScholarGoogle Scholar
  7. Buitelaar, P., Cimiano, P., Frank, A., Racioppa, S. SOBA: SmartWeb Ontology-based Annotation. Proceedings of the Demo Session at the International Semantic Web Conference. Athens, Greece, 2006Google ScholarGoogle Scholar
  8. Buzzetto-More, N. A. (ed.) Advanced Principles of Effective e-Learning. Informing Science Press, CA, USA, 2007.Google ScholarGoogle Scholar
  9. Carstensen, K.-U., Ebert, Ch., Endriss, C., Jekat, S., Klabunde, R., and Langer, H. Computerlinguistik und Sprachtechnologie. (2nd edition). Spektrum Akademischer Verlag, Munich, 2004.Google ScholarGoogle Scholar
  10. Cunningham, H., Wilks, Y., Gaizauskas, R. J. GATE -- a General Architecture for Text Engineering. Proceedings of the 16th International Conference on Computational Linguistics (COLING), pp. 1057--1060. Copenhagen, Denmark, August 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Doddington, G., Mitchell, A., Przybocki, M., Ramshaw, L., Strassel, S., Weischedel, R. The Automatic Content Extraction (ACE) Program -- Tasks, Data, and Evaluation. Proceedings of the International Conference on Language Resources and Evaluation, pp. 837--840. Lisbon, Portugal, 2004.Google ScholarGoogle Scholar
  12. Domjan, M. (ed.): The Principles of Learning and Behavior. 6th ed., Wadsworth, CA, USA, 2009.Google ScholarGoogle Scholar
  13. Francopoulo, G., Bel, N., George, M., Calzolari, N., Monachini, M., Pet, M., Soria, C. Lexical Markup Framework: ISO standard for semantic information in NLP lexicons. Tubingen, Germany, 2007.Google ScholarGoogle Scholar
  14. Frank, A., Becker, M., Crysmann, B., Kiefer, B., Schäfer, U. Integrated Shallow and Deep Parsing: TopP meets HPSG. Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 104--111, Sapporo, Japan, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Gamper, J., Knapp, J. A Review of Intelligent CALL Systems. Computer Assisted Language Learning, Volume 15, Number 4, October 2002, pp. 329--342, 2002.Google ScholarGoogle Scholar
  16. Gildea, D. and Martha Palmer, M. The Necessity of Parsing for Predicate Argument Recognition. Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 239--246, Philadelphia, USA, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Greene, C. E., Keogh, K., Koller, T., Wagner, J., Ward M., van Genabith J. Using NLP technology in CALL. In: InSTIL/ICALL 2004 Symposium on Computer Assisted Learning, 17--19 June, ISBN 88-8098-202-8, Venice, Italy, 2004.Google ScholarGoogle Scholar
  18. Huang, Chu-ren, Calzolari, N., Gangemi, A., Lenci, A., Oltramari, A., Prevot, L. Ontology and the Lexicon: A Natural Language Processing Perspective. Cambridge University Press, Cambridge, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  19. Indurkhya, I. (ed.), Damerau, F. J. (ed.). Handbook of Natural Language Processing. Chapman & Hall/CRC Machine Learning & Pattern Recognition, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kingsbury, P. and Palmer, M. From Treebank to PropBank. Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC). Las Palmas, Canary Islands, Spain, 2002.Google ScholarGoogle Scholar
  21. Levy, M. CALL: context and conceptualisation, Oxford: Oxford University Press, 1997.Google ScholarGoogle Scholar
  22. Meurers, D., Ziai, R., Amaral, L., Boyd, A., Dimitrov, A., Metcalf, V., Ott, N. Enhancing Authentic Web Pages for Language Learners. Proceedings of the 5th Workshop on Innovative Use of NLP for Building Educational Applications, NAACL-HLT 2010, Los Angeles, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Meurers D. Natural Language Processing and Language Learning. Encyclopedia of Applied Linguistics, edited by Carol A. Chapelle. Blackwell.Google ScholarGoogle Scholar
  24. Mitkov R. The Oxford Handbook of Computational Linguistics. Oxford University Press, Oxford, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Palmer, M., Gildea, D. and Kingsbury, P. The Proposition Bank: A Corpus Annotated with Semantic Roles. Computational Linguistics Journal, no. 31:1. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Sag, I. A., Wasow, T. and Bender, E. M. Syntactic Theory (2nd edition). Stanford, CSLI Publications, 2003.Google ScholarGoogle Scholar
  27. Schäfer, U. Integrating Deep and Shallow Natural Language Processing Components -- Representations and Hybrid Architectures. Saarbrücken Dissertations in Computational Linguistics and Language, Vol. 22, DFKI GmbH and Computational Linguistics Department, Saarland University, Saarbrücken, Germany, 2007.Google ScholarGoogle Scholar
  28. Semple, A. Learning Theories and Their Influence on the Development and Use of Educational Technologies. Australian Science Teachers' Journal, Vol. 46, No 3, September 2000, p. 21--22, 24--28, 2000.Google ScholarGoogle Scholar
  29. Wahl, H., Winiwarter, W., Quirchmayr, G. Natural Language Processing Technologies for Developing a Language Learning Environment. Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services (iiWAS2010), pp. 379--386, Paris, France: ACM (2010) Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Wahl, H., Winiwarter, W., Quirchmayr, G.: Towards an intelligent integrated language learning environment. International Journal of Pervasive Computing and Communications, Vol. 7 No. 3, 2011 pp. 220--239 (2011)Google ScholarGoogle ScholarCross RefCross Ref
  31. Wahl, H., Winiwarter, W.: A Technological Overview of an Intelligent Integrated Computer-Assisted Language Learning (iiCALL) Environment. Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications (ED-MEDIA) 2011, pp. 3832--3837, Chesapeake, Lisbon, Portugal, June 27th -- July 1st (2011)Google ScholarGoogle Scholar
  32. Wahl, H., Winiwarter, W.: The Intelligent Integrated Computer-Assisted Language Learning (iiCALL) Environment -- Work in Progress. Proceedings 13th International Conference on Information Integration and Web-based Applications & Services (iiWAS2011), pp. 426--429, ACM ISBN: 978-1-4503-0784-0, Ho Chi Minh City, Vietnam: ACM (2011) Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A context-related vocabulary trainer in the integrated intelligent computer-assisted language learning (iiCALL) environment

                    Recommendations

                    Comments

                    Login options

                    Check if you have access through your login credentials or your institution to get full access on this article.

                    Sign in
                    • Published in

                      cover image ACM Other conferences
                      IIWAS '12: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
                      December 2012
                      432 pages
                      ISBN:9781450313063
                      DOI:10.1145/2428736

                      Copyright © 2012 ACM

                      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                      Publisher

                      Association for Computing Machinery

                      New York, NY, United States

                      Publication History

                      • Published: 3 December 2012

                      Permissions

                      Request permissions about this article.

                      Request Permissions

                      Check for updates

                      Qualifiers

                      • research-article

                    PDF Format

                    View or Download as a PDF file.

                    PDF

                    eReader

                    View online with eReader.

                    eReader