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September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
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September 7, 2026
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.

 

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Development of a Language Model for Automated Classification of English-Language Scientific Articles by SRSTI Codes

Automatic Documentation and Mathematical Linguistics. 2025. Vol. 59. No. 5. P. 287–293.
V. V. Zunin, A. I. Afonin, V. I. Anoshin, Fedorets O. V., A. Y. Romanov

The development of an artificial intelligence-based language model for classifying English-language scientific articles by SRSTI codes is described. This improves the processes of reviewing and indexing scientific publications. A pre-processed dataset of scientific articles was used for training and testing the models. An architecture for cascade classification was developed, and the performance of models with various parameters was evaluated. As a result of this research, a console application for automatic classification of large flows of scientific articles was created.

Research target: Computer Science Philology and Linguistics
Language: English
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DOI
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Keywords: automatic classificationBERT machine learningcascade classification methodSRSTI codes
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