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  • Русский Конструктикон: новый лингвистический ресурс, его устройство и специфика
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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.
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Русский Конструктикон: новый лингвистический ресурс, его устройство и специфика

С. 241–255.
Эндресен А. А., Жукова В. А., Мордашова Д. Д., Rakhilina E. V., Lyashevskaya O.

We present a new open-access electronic resource named the Russian Constructicon that offers a searchable database of Russian constructions accompanied by descriptions of their properties and illustrated with corpus examples. The project was carried out over the period 2016–2020 and at present contains an inventory of over 2200 multi-word constructions of Contemporary Standard Russian. We prioritize “partially schematic” constructions that lie between the two extremes of fully compositional syntactic sequences on the one hand and fully idiomatic (phraseological) expressions on the other hand. Constructions of this type are difficult to account for in terms of either lexicon or grammar alone, and are often underrepresented in reference works of Russian. A typical construction in our database contains a fixed part (anchor words) and an open slot that can be filled with a restricted set of lexemes. In this paper we first focus on key characteristics of this resource that make it different from existing constructicons of other languages. Second, we describe how the new interface will be designed and how it will serve the needs of both linguists and L2 learners of Russian. In particular, we discuss various search possibilities relevant for different users and those parameters that are available for specifying the retrieval output. An example of an entry is given to show how the information about each construction is structured and presented. Third, we provide an overview of our multi-level semantic classification of constructions. We argue that our system of semantic and syntactic tags subdivides our items into meaningful classes and smaller groups and eventually facilitates the identification of constructional families and clusters. This methodology works well in turning the initial list of constructions as unrelated units into a structured network and makes it possible to refine and expand the collected inventory of constructions in a systematic way.

Language: Russian
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Keywords: Russian languageConstruction GrammarГрамматика конструкцийRussian ConstructiconconstructicographyРусский Конструктикон

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Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17 июня — 20 июня 2020 г.)
Вып. 19(26). , М.: Изд-во РГГУ, 2020.
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