Advertisement

Semantic Recommendations and Topic Modeling Based on the Chronology of Romanian Literary Life

  • Laurentiu-Marian Neagu
  • Teodor-Mihai Cotet
  • Mihai DascaluEmail author
  • Stefan Trausan-Matu
  • Lucian Chisu
  • Eugen Simion
Conference paper
  • 20 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11984)

Abstract

As part of the Romanian Academy’s effort aimed at underlining the importance of events centered on national authors and writings across time, the Chronology of Romanian Literary Life (also referred to as CVLR) is a centralized text repository which contains all important literature-related events that occurred after World War II. The current work presents an approach to capture topics’ evolution across time and helps learners by recommending events from the chronology on a given topic, based on a subset of 24 years of the CVLR. Our method combines techniques from information retrieval, topic modeling using LDA (Latent Dirichlet Allocation), and recommender systems to improve e-learning centered on Romanian literature. The most frequent topics in each year are ranked in order to identify and visualize the main interests in literature across time periods. Recommendations are performed in order to facilitate the exploration of the chronology, as it is currently indexed only by event dates.

Keywords

Analysis of the chronology of Romanian literary life Information retrieval Topic modeling Latent Dirichlet Allocation 

Notes

Acknowledgements

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III 54PCCDI ⁄ 2018, INTELLIT – “Prezervarea și valorificarea patrimoniului literar românesc folosind soluții digitale inteligente pentru extragerea și sistematizarea de cunoștințe”.

References

  1. 1.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  2. 2.
    Persen, W.C.A.R.: A Recommender System for Collaborative Knowledge. books.google.com 1 (2009)Google Scholar
  3. 3.
    Dagadita, M., Bancu, C., Dascalu, M., Dobre, C., Trausan-Matu, S., Florea, A.M.: ARSYS - article recommender system. In: 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2012). IEEE, Timisoara (2012)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, p. 845. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-85820-3CrossRefzbMATHGoogle Scholar
  6. 6.
    Tang, T.Y., McCalla, G.: Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. Int. J. E-learn. 4, 105–129 (2005)Google Scholar
  7. 7.
    Zaiane, O.R.: Building a recommender agent for e-learning systems. In: International Conference on Computers in Education, Proceedings, p. 5 (2002)Google Scholar
  8. 8.
    Klasnja-Milicevica, A., Boban Vesina, M.I., Budimac, Z.: E-Learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56, 885–899 (2011)CrossRefGoogle Scholar
  9. 9.
    Hoffman, M., Bach, F.R., Blei, D.M.: Online learning for Latent Dirichlet Allocation. In: Advances in Neural Information Processing Systems, p. 10 (2010)Google Scholar
  10. 10.
    Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399–408. ACM (2015)Google Scholar
  11. 11.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender Systems in Technology Enhanced Learning. Springer, New York (2014).  https://doi.org/10.1007/978-1-4939-0530-0CrossRefGoogle Scholar
  12. 12.
    Neagu, L.-M., Cotet, T.-M., Dascalu, M., Trausan-Matu, S., Badescu, L., Simion, E.: Semantic author recommendations based on their biography from the General Romanian Dictionary of Literature. In: 7th International Workshop on Semantic and Collaborative Technologies for the Web, in Conjunction with the 15th International Conference on eLearning and Software for Education (eLSE 2019), pp. 165–172. “CAROL I” National Defence University Publishing House, Bucharest (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laurentiu-Marian Neagu
    • 1
  • Teodor-Mihai Cotet
    • 1
  • Mihai Dascalu
    • 1
    Email author
  • Stefan Trausan-Matu
    • 1
  • Lucian Chisu
    • 2
  • Eugen Simion
    • 2
  1. 1.University Politehnica of BucharestBucharestRomania
  2. 2.The “G. Călinescu” Institute of Literary History and TheoryRomanian AcademyBucharestRomania

Personalised recommendations