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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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Exploring Semantic Concreteness and Abstractness for Metaphor Identification and Beyond

P. 33–47.
Badryzlova Y.

The paper presents a method for computing indexes of semantic concreteness and abstractness in two languages (Russian and English). These indexes are used in metaphor identification experiments in both languages; the results are either comparable to or surpass pervious work and the baselines. We analyze the obtained indexes of concreteness and abstractness to see how they align with the linguistic intuitions about the corresponding semantic categories. The results of the analysis may have broader implications for computational studies of the semantics of concreteness and abstractness.

Language: English
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Keywords: semanticsmachine learningdistributional semanticsmetaphor identificationconcretenessabstractness

In book

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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