Extracting Dependency Relations from Digital Learning Content

  • Giovanni Adorni
  • Felice Dell’Orletta
  • Frosina Koceva
  • Ilaria Torre
  • Giulia Venturi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 806)

Abstract

Digital Libraries present tremendous potential for developing e-learning applications, such as text comprehension and question-answering tools. A way to build this kind of tools is structuring the digital content into relevant concepts and dependency relations among them. While the literature offers several approaches for the former, the identification of dependencies, and specifically of prerequisite relations, is still an open issue. We present an approach to manage this task.

Keywords

Prerequisite relationship Concept extraction Graph mining 

Notes

Acknowledgements

The authors thank prof. Carlo Tasso for making available Distiller system for concept extraction during the initial experiments of the described methodology.

References

  1. 1.
    Basaldella, M., Chiaradia, G., Tasso, C.: Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction. In: COLING, pp. 804–814 (2016)Google Scholar
  2. 2.
    Bonin, F., Dell’Orletta, F., Venturi, G., Montemagni, S.: A contrastive approach to multi-word term extraction from domain corpora. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (2010)Google Scholar
  3. 3.
    Dell’Orletta, F., Venturi, G., Cimino, A., Montemagni, S.: T2k\(^2\): a system for automatically extracting and organizing knowledge from texts. In: Proceedings of 9th International Conference on Language Resources and Evaluation, pp. 2062–2070 (2014)Google Scholar
  4. 4.
    Dunning, T.: Accurate methods for the statistics of surprise and coincidence. Comput. Linguist. 19(1), 61–74 (1993)Google Scholar
  5. 5.
    Frantzi, K., Ananiadou, S.: The C-value/NC value domain independent method for multi-word term extraction. J. NLP 6(3), 145–179 (1999)Google Scholar
  6. 6.
    Gagné, R.M.: Learning hierarchies. In: Merrill, M.D. (ed.) Instructional Design: Readings, pp. 118–131. Prentice-Hall, Englewood Cliffs (1968, 1971)Google Scholar
  7. 7.
    Kowata, J.H., Cury, D., Boeres, M.: A review of semi-automatic approaches to build concept maps. In: Proceedings of the 4th Conference on Concept Mapping, pp. 40–48 (2010)Google Scholar
  8. 8.
    Liang, C., Wu, Z., Huang, W., Giles, C.L.: Measuring prerequisite relations among concepts. In: EMNLP, pp. 1668–1674 (2015)Google Scholar
  9. 9.
    Pan, L., Li, C., Li, J., Tang, J.: Prerequisite relation learning for concepts in MOOCs. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, Long Papers, vol. 1, pp. 1447–1456 (2017)Google Scholar
  10. 10.
    Wang, S., Ororbia, A., Wu, Z., Williams, K., Liang, C., Pursel, B., Giles, C.L.: Using prerequisites to extract concept maps from textbooks. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 317–326 (2016)Google Scholar
  11. 11.
    Yoon, W.C., Lee, S., Lee, S.: Burst analysis of text document for automatic concept map creation. In: Ali, M., Pan, J.-S., Chen, S.-M., Horng, M.-F. (eds.) IEA/AIE 2014. LNCS (LNAI), vol. 8482, pp. 407–416. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07467-2_43 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Giovanni Adorni
    • 1
  • Felice Dell’Orletta
    • 2
  • Frosina Koceva
    • 1
  • Ilaria Torre
    • 1
  • Giulia Venturi
    • 2
  1. 1.Department of Informatics, Bioengineering, Robotics and Systems EngineeringUniversity of GenoaGenoaItaly
  2. 2.Istituto di Linguistica Computazionale Antonio Zampolli (ILCCNR)PisaItaly

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