METHODOLOGICAL ASPECTS OF TEACHING STUDENTS TO CREATE DIGITAL FREQUENCY DICTIONARIES

Keywords: frequency dictionary, Python programming language, program code, teaching methods

Abstract

Frequency dictionaries are created to identify the most frequently used words in a natural language, a writer’s language, a particular literary work, etc. These dictionaries are used while learning foreign languages, creating other kinds of dictionaries, conducting language experiments, carrying out research in the fields of lexical semantics, psycholinguistics, morphology, etc. and designing applications focused on natural language processing. Information and communication technology and access to large digital linguistic corpora and national word corpora based on the subtitles of movies and TV shows have accelerated research in the field of statistical text processing. Due to frequency dictionaries being widely used in various fields of activity and their creation offering the solution to a wide range of linguistic issues, it seems reasonable to analyze the technology of creating such dictionaries in the process of training future specialists in Applied Linguistics and Computer Science as well as teachers of Computer Science. The paper runs about the methods of teaching students to create frequency dictionaries. An algorithm of creating a frequency dictionary is offered. The peculiarities of realizing each stage of the algorithm with the help of the popular Python programming language, which has an open code and extensive libraries, are analyzed. The author provides program codes and justifies the use of corresponding modules, string methods, functions, constants, data structures, and regular expressions. The proposed methodology is aimed at 1) raising the students’ motivation to study and 2) revealing the practical significance of the acquired programming methods and techniques. The author argues that the approach presented in this paper can be of advantage to teachers of computer-related courses.

Author Biography

Olga V. Riezina , Volodymyr Vynnychenko Central Ukrainian State Pedagogical University

PhD of Pedagogical Sciences, Associate Professor at the Department of Computer Science and Technology

References

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REFERENCES (TRANSLATED AND TRANSLITERATED)

M. Brysbaert, and B. New, " Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English," Behavior Research Methods, vol. 41, no. 4, pp. 977-990, Nov. 2009. doi: https://doi.org/10.3758/BRM.41.4.977. (in English)

M. Davies, and D. Gardner, A Frequency Dictionary of American English: Word Sketches, Collocates and Thematic Lists. London, UK: Routledge, 2010. (in English)

C. Lehmann, "Frequency dictionary", Christianlehmann.eu, 2018. [Online]. Available: https://www.christianlehmann.eu/ling/ling_meth/ling_description/lexicography/frequency_dict.html. Accessed on: Jul. 17, 2018. (in English)

Word frequency data, Wordfrequency.info, 2018. [Online]. Available: https://www.wordfrequency.info/uses.asp. Accessed on: Jul. 17, 2018. (in English)

J. DeRocher, M. Miron, S. Patten and C. Pratt, The Counting of Words: A Review of the History, Techniques and Theory of Word Counts with Annotated Bibliography. New York, NY, USA: Syracuse University Research Corporation, 1973. (in English)

E. Tseng, S. A. Gandhi, A. D. Kramer, and L. S. Clair, "Blending customized user dictionaries based on frequency of usage - Google Patents", Patents.google.com, 2018. [Online]. Available: https://patents.google.com/patent/US9977774B2/en. Accessed on: Jul. 17, 2018. (in English)

R. Meier, J. Hausmann, H. Urbschat, and T. Wanschura, "Hierarchical Dictionary with Statistical Filtering Based on Word Frequency - Google Patents", Patents.google.com, 2018. [Online]. Available: https://patents.google.com/patent/US20170220679A1/en. Accessed on: Jul. 17, 2018. (in English)

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Data Structures – Python 3.7.0 documentation, Docs.python.org, 2018. [Online]. Available: https://docs.python.org/3/tutorial/datastructures.html. Accessed on: Jul. 18, 2018. (in English)

S. Bird, E. Klein, and E. Loper,"Accessing Text Corpora and Lexical Resources", in Natural language processing with Python – Analyzing Text with the Natural Language Toolkit, Nltk.org, 2018. [Online]. Available: https://www.nltk.org/book/ch02.html. Accessed on: Jul. 18, 2018. (in English)

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Published
2019-09-21
Section
ICT and learning tools in the higher education establishments