Труды КНЦ (Технические науки вып.3/2025(16))

24. Xiong J., Hswen Y., Naslund J. A. Digital surveillance for monitoring environmental health threats: a case study capturing public opinion fromTwitter about the 2019 Chennai water crisis. International Journal o fEnvironmental Research and Public Health, 2020, 17 (14), 5077. doi: 10.3390/ijerph17145077. 25. Lee Y., Bradford B., Posch K. The effectiveness of big data-driven predictive policing: systematic review. Justice Evaluation Journal , 2024, 7 (2), pp. 127-160. 26. Rusnachenko N. L. Language models application in sentiment attitude extraction task. Proc. ISP RAS, 2021, 33 (3), pp. 199-222. 27. Shishaev M., Dikovitsky V., Pimeshkov V., Kuprikov N., Kuprikov M., Shkodyrev V. Extracting relations from texts using vector language models and a neural network classifier. PeerJ Computer Science, 2023, 9, e1636. 28. Wang Z. et al. History, development, and principles of large language models: an introductory survey. A I and Ethics , 2025, 5 (3), pp. 1955-1971. 29. Huwiler D., Stockinger K., Furst J. VersionRAG: Version-aware retrieval-augmented generation for evolving documents.2025. Available at: https://arxiv.org/pdf/2510.08109 (accessed 25.10.2025). 30. Sytko A. V. The speaker as a subject of deontics in political speech (based on the German and Russian languages). RUDN Journal o fLanguage Theory. Semiotics. Semantics, 2019, 10 (4), pp. 1003-1020. 31. RAS F. ruSciFact: open benchmark for verifying scientific facts in Russian. Proceedings o f the International Conference “Dialogue", 2025. 32. Huang K., Chen C. Subgraph generation applied in GraphSAGE to deal with imbalanced node classification. Soft Computing , 2024, 28 (17), pp. 10727-10740. 33. Fey M., Lenssen J.E. Fast graph representation learning with PyTorch Geometric. Available at: https://arxiv.org/pdf/1903.02428 (accessed 20.10.2025). 34. Lewis P. et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 2020, Vol. 33, pp. 9459-9474. 35. Pearl J. Causality . Cambridge, Cambridge University Press, 2009. Информация об авторе В. В. Диковицкий — кандидат технических наук, старший научный сотрудник. Information about the author V. V. Dikovitsky — Candidate of Science (Tech.), Senior Research Fellow. Статья поступила в редакцию 23.10.2025; одобрена после рецензирования 30.10.2025; принята к публикации 03.11.2025. The article was submitted 23.10.2025; approved after reviewing 30.10.2025; accepted for publication 03.11.2025. Труды Кольского научного центра РАН. Серия: Технические науки. 2025. Т. 16, № 3. С. 71-79. Transactions of the Kola Science Centre of RAS. Series: Engineering Sciences. 2025. Vol. 16, No. 3. P. 71-79. © Диковицкий В. В., 2025 79