Linguistic-cognitive modelling of IT-terms in translation: Frame-based approach

Mykyta Bondar
Abstract

The research relevance is determined by rapid development of information technologies and need for adequate translation of highly dynamic English information technology terminology into Ukrainian, which requires transitioning outside of formal lexical correspondence to ensure cognitive equivalence. The study aimed to conduct a comprehensive investigation of potential of frame analysis as a tool for linguistic-cognitive modelling in translation of information technology terminology. Reliability of results obtained was ensured by use of general scientific and linguistic methods: descriptive method, contextual-interpretative analysis, frame-based modelling, contrastive analysis, and corpus linguistics approaches. Based on an analysis of a bilingual parallel corpus extracted from official documentation of Blender software, relevant features of technical texts were interpreted, and frame structures of selected terms from domains of 3D modelling and animation were reconstructed and compared. The main focus of the study was assessment of cognitive equivalence and identification of implicit conceptual discrepancies between source and target languages. The study established that established information technology vocabulary, particularly within geometric, temporal, and interface-related domains, demonstrates a high level of frame correspondence and structural stability in target language. Conversely, translation of innovative, multi-component, and procedurally oriented terms frequently results in significant cognitive asymmetries. Frame shifts were disclosed primarily in loss of procedural characteristics, alteration of functional syntax through passive verb constructions, and unjustified transference of digital instrument concepts into tangible physical domains. Fundamental criteria for preservation of conceptual integrity of multiword terms by accurately synthesising constituent frame slots were established. Practical significance of the study conducted was primarily by the possible use of results in systematisation and refinement of approaches to domain-specific lexicography, improvement of professional training of technical translators, and integration of structured conceptual networks into neural machine translation systems

Keywords

cognitive equivalence; conceptual mapping; 3D modelling nomenclature; domainspecific vocabulary; procedural semantics

Suggested citation
Bondar, M. (2025). Linguistic-cognitive modelling of IT-terms in translation: Frame-based approach. International Journal of Philology, 16(4), 41-53. https://doi.org/10.31548/philolog/4.2025.41
References
  1. Blender Translate. (n.d.). Retrieved from https://translate.blender.org/projects/blendermanual/manual/.
  2. Bondarenko, A. (2022). The features of frame interpretation in translation (on the material of Ukrainian and Crimean Tatar languages). Linguistic and Conceptual Views of the World, 71(1), 16-24. doi: 10.17721/2520-6397.2022.1.02.
  3. Czulo, O., Torrent, T.T., Matos, E.E.D.S., da Costa, A.D., & Kar, D. (2019). Designing a framesemantic machine translation evaluation metric. In Proceedings of the human-informed translation and interpreting technology workshop (HiT-IT 2019) (pp. 28-35). Varna: Incoma Ltd. doi: 10.26615/issn.2683-0078.2019_004.
  4. Faber, P., & Cabezas-García, M. (2019). Specialized knowledge representation: From terms to frames. Research in Language, 17(2), 197-211. doi: 10.2478/rela-2019-0012.
  5. Faber, P., & Reimerink, A. (2019). Framing terminology in legal translation. International Journal of Legal Discourse, 4(1), 15-46. doi: 10.1515/ijld-2019-2015.
  6. Giacomini, L. (2018). Frame-based lexicography: Presenting multiword terms in a technical e-dictionary. In Proceedings of the XVIII EuraLex international congress (pp. 309-318). Ljubljana: Ljubljana University Press.
  7. Giacomini, L., & Schäfer, J. (2020). Computational aspects of frame-based meaning representation in terminology. In Proceedings of the 6th international workshop on computational terminology (pp. 80-84). Marseille: European Language Resources Association.
  8. Hamamoto, H. (2023). How to obtain translation equivalence of culturally specific concepts in a target language. Translation and Translanguaging in Multilingual Contexts, 9(1), 8-21. doi: 10.1075/ttmc.00099.ham.
  9. Hinrichs, N., Foradi, M., Yousef, T., Hartmann, E., Triesch, S., Kaßel, J., & Pein, J. (2022). Embodied metarepresentations. Frontiers in Neurorobotics, 16, article number 836799. doi: 10.3389/fnbot.2022.836799.
  10. Hitcheva, D. (2025). Solving translation problems with terminological verb collocations: Case studies with building materials. Annual of University of Architecture, Civil Engineering and Geodesy, 58(1), 191-203. doi: 10.71167/uaceg.2025.580114.
  11. L’Homme, M.-C. (2018). Maintaining the balance between knowledge and the lexicon in terminology. Lexicography, 4(1), 3-21. doi: 10.1007/s40607-018-0034-1.
  12. Liu, F. (2025). Term dictionary automatic extraction algorithm based on BiLSTM. In 2025 international conference on intelligent systems and computational networks (ICISCN). Bidar: IEEE. doi: 10.1109/ICISCN64258.2025.10934268.
  13. Maslova, T., & Fedorenko, S. (2022). Cognitive approach to interdisciplinary research of terminology. Advanced Linguistics, 9, 43-50. doi: 10.20535/2617-5339.2022.9.259836.
  14. Moslem, Y., Romani, G., Molaei, M., Kelleher, J.D., Haque, R., & Way, A. (2023). Domain terminology integration into machine translation: Leveraging large language models. In Proceedings of the eighth conference on machine translation (pp. 902-911). Singapore: Association for Computational Linguistics. doi: 10.18653/v1/2023.wmt-1.82.
  15. Nwachukwu, J.F. (2024). Theoretical modelling of the translation process. Cadernos de Tradução, 44(1), 1-13. doi: 10.5007/2175-7968.2024.e91730.
  16. Pan, Y. (2020). Corpus linguistics approaches to trainee translators’ framing practice in news translation. The International Journal of Translation and Interpreting Research, 12(1), 90-114. doi: 10.12807/ti.112201.2020.a06.
  17. Resi, R. (2024). Concept systems and frames: Detecting and managing terminological gaps between languages. Applied Ontology, 19(1), 47-71. doi: 10.3233/AO-230046.
  18. Rishniak, H. (2021). Social frame in cognitive linguistics and its potential in translation studies. Grail of Science, 6, 226-229. doi: 10.36074/grail-of-science.25.06.2021.038.
  19. Rojas-Garcia, J. (2025). Powerful variables for knowledge representation and bracketing prediction. Translation and Translanguaging in Multilingual Contexts, 11(1), 5-30. doi: 10.1075/ttmc.00151.roj.
  20. Sánchez Cárdenas, B. (2024). Extracting semantic frames from specialized corpora for lexicographic purposes. Círculo de Lingüística Aplicada a La Comunicación, 99, 163-177. doi: 10.5209/clac.90626.
  21. Sevastiuk, M.I. (2023). Rendering confrontation strategies in the process of political discourse translation: Cognitive modeling. International Humanitarian University Herald. Philology, 3(59), 148-154. doi: 10.32841/2409-1154.2023.59.3.34.
  22. Sullivan, K. (2023). Three levels of framing. WIREs Cognitive Science, 14(5), article number e1651. doi: 10.1002/wcs.1651.
  23. Van Dijk, T.A. (2023). Analyzing frame analysis: A critical review of framing studies in social movement research. Discourse Studies, 25(2), 153-178. doi: 10.1177/14614456231155080.
  24. Vicari, S. (2023). Frame semantic grammars: Where frame analysis meets linguistics to study collective action frames. Discourse Studies, 25(2), 309-318. doi: 10.1177/14614456231154737.
  25. Zakaria, I. (2017). Quantifying a successful translation: A cognitive frame analysis of (un)translatability. Linguistics Beyond and Within (LingBaW), 3, 229-244. doi: 10.31743/ lingbaw.5661.