Computational linguistics and its teaching in a technical university within the framework of the digital department

Cover Page

Cite item

Abstract

the article considers the problems of teaching computational linguistics at a technical university. The research group analyzes its experience and shares the results: how important it is to attract students' attention to issues of computational linguistics at the current stage of development of artificial intelligence programs. Our department has extensive experience in the following areas: 1) organizing training within digital departments in the humanities; 2) placing emphasis in the field of creating and analyzing large text arrays in the context of training future specialists with digital competencies; 3) cooperation between language departments and programming departments for the development of both tools for analyzing language databases and creating them; 4) educational and methodological aspects of teaching digital competencies in the implementation of translation for all areas of training, taking into account current trends in the university. Thanks to the qualification level, we can implement our knowledge and competencies in the practice of teaching the basics of computer linguistics of higher professional education – on the one hand, and in the educational and methodological sphere for students studying at KSPEU.

About the authors

G. F Lutfullina

Kazan State Power Engineering University

Email: gflutfullina@mail.ru

I. V Marzoeva

Kazan State Power Engineering University

Email: arigata@bk.ru

G. Z Gilyazieva

Kazan State Power Engineering University

Email: gilyazieva1978@mail.ru

I. P Nazarova

Kazan State Power Engineering University

Email: nazarova.nazira@yandex.ru

D. A Demidkina

Kazan State Power Engineering University

Email: daria.demidkina1@gmail.com

References

  1. Bowerman M. The "no negative evidence" problem: How do children avoid constructing an overly general grammar. Explaining language universals. 1988.
  2. Braine M.D.S. On two types of models of the internalization of grammars. The ontogenesis of grammar: A theoretical perspective. New York: Academic Press. 1971.
  3. Elman Jeffrey L. Learning and development in neural networks: The importance of starting small // Cognition. 1993. No 48 (1). P. 71 – 99. doi: 10.1016/0010-0277(93)90058-4
  4. Furuhashi S., Hayakawa Y. Lognormality of the Distribution of Japanese Sentence Lengths // Journal of the Physical Society of Japan. 2012. No 81 (3). P. 034004. doi: 10.1143/JPSJ.81.034004
  5. Gong T., Shuai L., Tamariz M., J?ger G.E. Scalas (ed.). Studying Language Change Using Price Equation and P?lya-urn Dynamics // Plos One. 2012. No 7 (3). doi: 10.1371/journal.pone.0033171
  6. Hutchins John: Retrospect and prospect in computer-based translation. Wayback Machine Proceedings of MT Summit VII. 1999. P. 30 – 44.
  7. Marcus M., Marcinkiewicz M. Building a large annotated corpus of English: The Penn Treebank". Computational Linguistics. 1993. No 19 (2). P. 313 – 330.
  8. Powers D.M.W., Turk C.C.R. Machine Learning of Natural Language. Springer-Verlag. 1989. ISBN 978-0-387-19557-5.
  9. Salvi G., Montesano L., Bernardino A., Santos-Victor J. Language bootstrapping: learning word meanings from the perception-action association // IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics. 2012. No 42 (3). P. 660 – 671. doi: 10.1109/TSMCB.2011.2172420. PMID: 22106152. S2CID 977486.
  10. Taylor Ann. Treebanks. Spring Netherlands. 2003. P. 5 – 22.
  11. Yogita Bansal. Insight to Computational Linguistics // International Journal. 2016. No 4.10. P. 94.

Supplementary files

Supplementary Files
Action
1. JATS XML

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).