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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
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Когнитивный термин «фрейм»: создание словарной статьи на базе специализированного текстового корпуса

Вестник Новосибирского государственного университета. Серия: Лингвистика и межкультурная коммуникация. 2022. Т. 20. № 4. С. 17–34.
Khomenko A., Куликова В. А., Babiy A., Казюлина М. С.

The study is devoted to the testing of a specialized texts corpus on the example of a group of cognitive linguistics terms with the hypernym frame. The corpus includes a subcorpus of scientific texts and a subcorpus of journalistic texts. The first one is represented by 15 journals indexed in the RSCI; the second one is represented by 10 significant Russian media. The collected texts were lemmatized and tokenized, as well as automatically marked up using the Universal Dependencies standard. The corpus is used for creation a cognitive linguistics terms educational dictionary. This lexicographic source includes 60 relevant terms that are important in the process of mastering university disciplines within the cognitive sciences. The novelty of the approach is connected with the thesaurus-encyclopedic type of the dictionary, which allows to describe the word both as a term (a minimal component of scientific knowledge) and as a unit of a scientific text in its various collocations and ontological relations (synonymy, quasi-synonymy, class-subclass; polysemy, etc.). The basis for describing the systemic relations of terms is corpus statistics: analysis of concordances, collocations, n-grams. The results of using a specialized texts corpus are presented on the example of a terminological field with the dominant “frame”. With the help of concordance lists, contexts of term usage are revealed, its derivational relations are established. The semantic and grammatical relationships of the term are characterized throughout n-grams analysis. Synonyms, hyponyms, hyperonyms of the term are described based on the study of collocations. The Dashinimaeva-Wang hypothesis about the links between the terms concept, frame, gestalt, image was also tested. The semantic transformations of the scientific term in the media discourse were briefly characterized. The systemic relations of terms identified on the basis of corpus statistics are also confirmed by the method of interpretative analysis of contexts obtained from concordance lists. The data of automatically selected associative measures also largely agree with the results of the associative experiment carried out in the framework of the study.

Research target: Philology and Linguistics
Language: Russian
Full text
DOI
Keywords: когнитивная лингвистикалексикографиятерминологиякорпусная лингвистикаcorpus linguisticslexicographyterminologyCognitive Linguisticsspecial corpusспециальный корпус
Publication based on the results of:
Проект учебного словаря-справочника терминов когнитивной лингвистики (2022)
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