Language Technology for eLearning

  • Paola Monachesi
  • Lothar Lemnitzer
  • Kiril Simov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4227)


Given the huge amount of static and dynamic content created for eLearning tasks, the major challenge for extending their use is to improve the effectiveness of retrieval and accessibility by making use of Learning Management Systems. The aim of the European project Language Technology for eLearning is to tackle this problem by providing Language Technology based functionalities and by integrating semantic knowledge to facilitate the management, distribution and retrieval of the learning material.


Learning Material Domain Ontology Semantic Knowledge Learn Management System Language Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Final Draft, Standard for Learning Object Metadata, IEEE, 2002 – P1484.12.1Google Scholar
  2. 2.
    Church, K., Gale, W.: Poisson mixtures. Natural Language Engineering 1(2), 163–190 (1995)CrossRefGoogle Scholar
  3. 3.
    Church, K., Kenneth, W., Gale, W.: Inverse Document Frequency (IDF): A Measure of Deviations from Poisson. In: Proc. of Third Workshop on Very Large Corpora (1995)Google Scholar
  4. 4.
    Sarkar, A., Garthwaite, P.H., De Roeck, A.: A Bayesian Mixture Model for Term Re-occurrence and Burstiness. In: Proc. of the Ninth Conference on Computational Natural Language Learning (CoNLL 2005), Ann Arbor, Michigan, pp. 48–55. ACL (2005)Google Scholar
  5. 5.
    Hulth, A.: Improved Automatic Keyword Extraction Given More Linguistic Knowledge. In: Proc. of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 216–223 (2003)Google Scholar
  6. 6.
    Miliaraki, S., Androutsopoulos, I.: Learning to identify single-snippet answers to definition questions. In: 20th International Conference on Computational Linguistics (COLING 2004), pp. 1360–1366 (2004)Google Scholar
  7. 7.
    Blair-Goldensohn, S., McKeown, K., Schlaikjer, A.H.: Answering definitional questions: a hybrid approach. New directions in question answering, 47–58 (2004)Google Scholar
  8. 8.
    Liu, B., Chin, C.W., Ng, H.T.: Mining topic-specific concepts and definitions on the web. In: Proc. 12th Internat. Conf. on World Wide Web, WWW 2003, pp. 251–260 (2003)Google Scholar
  9. 9.
    Fahmi, I., Bouma, G.: Learning to Identify Definitions using Syntactic Features. In: Proceedings of the EACL 2006 Workshop on Learning Structured Information in Natural Language Applications (2006)Google Scholar
  10. 10.
    Klavans, J., Muresan, S.: Evaluation of the DEFINDER System for Fully Automatic Glossary Construction. In: Proc. of AMIA Symposium 2001 (2001)Google Scholar
  11. 11.
    Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., Schneider, L.: Wonder Web Deliverable D17. The WonderWeb Library of Foundational Ontologies and the DOLCE ontology. Preliminary Report (ver. 2.0, 15-08-2002) (2002)Google Scholar
  12. 12.
    Kiryakov, A., Simov, K.: Ontologically Supported Semantic Matching. In: The Proceedings of NoDaLiDa 1999 (Nordic Conference on Computational Linguistics), Trondheim, Norway (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paola Monachesi
    • 1
  • Lothar Lemnitzer
    • 2
  • Kiril Simov
    • 3
  1. 1.Uil-OTSUtrecht UniversityJK UtrechtThe Netherlands
  2. 2.University of TübingenTübingenGermany
  3. 3.LML, IPP, Bulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations