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  • Новые технологические тренды: выявление в текстах на базе использования гибридных моделей и анализа временных рядов паттернов данных
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Subject
News
July 24, 2026
‘I Like Self-Fulfilling Prophecies
Andrey Vorchik studies happiness, delivers popular science lectures, and believes that science should address social issues as well. In an interview for the Young Scientists of HSE University project, he spoke about how emotions influence decision-making, the Bermuda Triangle formed by the bathroom, refrigerator, and bed, and the ideal formula for education.
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.

 

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Новые технологические тренды: выявление в текстах на базе использования гибридных моделей и анализа временных рядов паттернов данных

Информационно-измерительные и управляющие системы. 2013. Т. 11. № 5. С. 25–34.
Khoroshevsky V. F.

New technological trends identification is one of the most sophisticated, as well as the most important, tasks in the domain of S&T analysis. Nowadays, the leading methodologies within the domain are focused mainly on technological roadmapping, Foresight, data patterns and time series analysis, which is used to specify current and projected trends. The paper presents intelligent tools for trend identification in texts collections with hybrid approach based on the integration of classical statistical methods and the methods of information extraction. Several existing approaches are combined to be used for multilingual text collections of various genres. Ontologies driving text processing as well as documents’ characteristic vectors containing multiword terms are used. The results of statistical analysis of document collections are presented in the form of data patterns time series that are analyzed with structural methods of image analysis. OWL representation of extensional part of the trend ontological model is generated.

Research target: Philology and Linguistics Computer Science
Priority areas: business informatics
Language: Russian
Keywords: технологический трендизвлечение информации из текстовгибридный подход
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