Preview

Russian Digital Libraries Journal

Advanced search

Creating Pseudowords Generator and Classifier of Their Similarity with Words from Russian Dictionary using Machine Learning

https://doi.org/10.26907/1562-5419-2025-28-1-145-162

Abstract


In this article, a pseudoword is defined as a unit of speech or text that appears to be a real word in Russian but actually has no meaning. A real or natural word is a unit of speech or text that has an interpretation and is presented in a dictionary. The paper presents two models for working with the Russian language: a generator that creates pseudowords that resemble real words, and a classifier that evaluates the degree of similarity between the entered sequence of characters and real words. The classifier is used to evaluate the results of the generator. Both models are based on recurrent neural networks with long short-term memory layers and are trained on a dataset of Russian nouns. As a result of the research, a file was created containing a list of pseudowords generated by the generator model. These words were then evaluated by the classifier to filter out those that were not similar enough to real words. The generated pseudowords have potential applications in tasks such as name and branding creation, layout design, art, crafting creative works, and linguistic studies for exploring language structure and words.

About the Authors

Kirill Alekseevich Romadanskiy
Kazan (Volga region) Federal University
Russian Federation


Artemii Evgenyevich Akhaev
Kazan (Volga region) Federal University
Russian Federation


Tagmir Radikovich Gilyazov
Kazan (Volga region) Federal University
Russian Federation


References

1. Sagiroglu S., Sinanc D. Big Data: A Review // 2013 International Conference on Collaboration Technologies and Systems (CTS). 2013. P. 42–47.

2. Shim K. MapReduce algorithms for Big Data Analysis // Proceedings of the VLDB Endowment. 2012. V. 5. No. 12. P. 2016–2017.

3. Строев В.В., Тихонов А.И. Применение технологий Data Mining для поиска соответствий закономерностей развития в больших массивах веб-данных на основе инструментов анализа Big Data // E-Management. 2022. Т. 5. N 4. С. 4–11.

4. Kim J., Shin S., Bae K., Oh S. Can AI be a content creator? Effects of content creators and information delivery methods on the psychology of content consumers // Telematics and Informatics. 2020. V. 55. P. 101452.

5. Лалетина А.О. Языковая норма в эпоху глобализации // Ученые записки Казанского университета. Серия Гуманитарные науки. 2011. Т. 153. № 6. С. 219–226.

6. Москалёва М.В. Неологизмы и проблема их изучения в современном русском языке // Известия РГПУ им. А. И. Герцена. 2008. № 80. С. 246–250.

7. Дмитриева Д.Д. Изучение словообразования на занятиях по русскому языку как иностранному // Балтийский гуманитарный журнал. 2020. Т. 9. № 1(30). С. 47–49.

8. Shipley D., Hooky G.J., Wallace S. The brand name Development Process // International Journal of Advertising. 1988. V. 7. No. 3. P. 253–266.

9. Mazzola G., Carapezza M., Chella A., Mantoan D. Artificial Intelligence in Art Generation: An Open Issue // Image Analysis and Processing – ICIAP 2023 Workshops. 2023. V. 14366. P. 258–269.

10. Jarmulowicz L., Taran V.L. Lexical morphology // Topics in Language Disorders. 2013. V. 33. No. 1. P. 57–72.

11. Iqbal T., Qureshi S. The survey: Text generation models in deep learning // Journal of King Saud University - Computer and Information Sciences. 2022. V. 34. No. 6. P. 2515–2528.

12. Yu Y., Si X., Hu C., Zhang J. A review of Recurrent Neural Networks: LSTM cells and network architectures // Neural Computation. 2019. Т. 31. No. 7. P. 1235–1270.

13. Ketkar N. Introduction to Keras // Deep Learning with Python. Berkeley, CA: Apress, 2017. P. 97–111.

14. Helms M. Badestrand/Russian-Dictionary: Dataset of nouns, verbs, adjectives and others from my Russian dictionary website OpenRussian.org. [Электронный ресурс]. URL: https://github.com/Badestrand/russian-dictionary (дата обращения: 17.10.2023).

15. Rodríguez P., Bautista M.A., Gonzàlez J., Escalera S. Beyond one-hot encoding: Lower dimensional target embedding // Image and Vision Computing. 2018. V. 75. P. 21–31.

16. Mao A., Mohri M., Zhong Y. Cross-entropy loss functions: Theoretical analysis and applications // Proceedings of the 40th International Conference on Machine Learning. 2023. V. 202. P. 23803–23828.

17. Manaswi N.K. Understanding and Working with Keras // Deep Learning with Applications Using Python. Berkeley, CA: Apress, 2018. P. 31–43.


Review

For citations:


Romadanskiy K.A., Akhaev A.E., Gilyazov T.R. Creating Pseudowords Generator and Classifier of Their Similarity with Words from Russian Dictionary using Machine Learning. Russian Digital Libraries Journal. 2025;28(1):145-162. (In Russ.) https://doi.org/10.26907/1562-5419-2025-28-1-145-162

Views: 25


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1562-5419 (Online)