Visualizing Textbook Concepts: Beyond Word Co-occurrences (original) (raw)

Abstract

In this paper, we present a simple and elegant algorithm to extract and visualize various concept relationships present in sections of a textbook. This can be easily extended to develop visualizations of entire chapters or textbooks, thereby opening up opportunities for developing a range of visual applications for e-learning and education in general. Our algorithm creates visualizations by mining relationships between concepts present in a text by applying the idea of transitive closure rather than merely counting co-occurrences of terms. It does not require any thesaurus or ontology of concepts. We applied the algorithm to two textbooks - Theory of Computation and Machine Learning - to extract and visualize concept relationships from their sections. Our findings show that the algorithm is capable of capturing deep-set relationships between concepts which could not have been found by using a term co-occurrence approach.

Similar content being viewed by others

References

  1. Erkan, G., Radev, G.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22(1), 457–479 (2004)
    Article Google Scholar
  2. Wan, X., Yang, J., Xiao, J.: Towards an iterative reinforcement of simultaneous document summarization and keyword extraction. In: Proceedings of ACL 2007, Prague, pp. 552–559 (2007)
    Google Scholar
  3. Cao, C., et al.: Progress in the development of national knowledge infrastructure. J. Comput. Sci. Technol. 17(5), 523–534 (2002)
    Google Scholar
  4. Liu, H., Singh, P.: ConceptNet — a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004)
    Article Google Scholar
  5. Cambria, E., Hussain, A., Havasi, C., Eckl, C., Munro, J.: Towards crowd validation of the UK national health service. In: Websci 2010 (2010)
    Google Scholar
  6. Yuntao, Z., Ling, G., Yongcheng, W., Zhonghang, Y.: An effective concept extraction method for improving text classification performance. Geo-spatial Inf. Sci. 6(4), 66–72 (2003)
    Article Google Scholar
  7. Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N.: Dependency-based semantic parsing for concept-level text analysis. In: Gelbukh, A. (ed.) CICLing 2014. LNCS, vol. 8403, pp. 113–127. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54906-9_10
    Chapter Google Scholar
  8. Guerra, J., Sosnovsky, S., Brusilovsky, P.: When one textbook is not enough: linking multiple textbooks using probabilistic topic models. In: Hernández-Leo, D., Ley, T., Klamma, R., Harrer, A. (eds.) EC-TEL 2013. LNCS, vol. 8095, pp. 125–138. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40814-4_11
    Chapter Google Scholar
  9. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
    MATH Google Scholar
  10. Šimko, M., Bieliková, M.: Automatic concept relationships discovery for an adaptive E-course. In: International Conference on Educational Data Mining (2009)
    Google Scholar
  11. Mahesh, K.: Theory of Computation: A Problem-Solving Approach. Wiley, New Delhi (2012)
    Google Scholar
  12. Harrington, P.: Machine Learning in Action. Manning Publications Co., Greenwich (2012)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. Center for Knowledge Analytics and Ontological Engineering (KAnOE), PES Institute of Technology, Bengaluru, India
    Chandramouli Shama Sastry & Darshan Siddesh Jagaluru
  2. School of Computing and Decision Sciences, Great Lakes International University, Sricity, India
    Kavi Mahesh

Authors

  1. Chandramouli Shama Sastry
  2. Darshan Siddesh Jagaluru
  3. Kavi Mahesh

Corresponding author

Correspondence toChandramouli Shama Sastry .

Editor information

Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico
    Alexander Gelbukh

Rights and permissions

© 2018 Springer Nature Switzerland AG

About this paper

Cite this paper

Sastry, C.S., Jagaluru, D.S., Mahesh, K. (2018). Visualizing Textbook Concepts: Beyond Word Co-occurrences. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7\_29

Download citation

Keywords

Publish with us