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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Term extraction for constructing subject index of educational scientific text

P. 143–152.
Bolshakova E. I., Ivanov K.

Subject index, or back-of-the-book index, is a device intended to provide an easy access to relevant fragments of a text document. Subject indexes usually contain particular single-word and multi-word terms from the corresponding documents. Such indexes are especially useful for reading large documents with specialized terminology, as well as educational texts in difficult scientific and technical areas. The central problem of back-of-the-book indexing is recognition of terms to be included into the index. The paper describes a method developed for extracting and filtering terms from a given educational scientific text, with the purpose of reliable term selection in computer indexing systems. The method is primarily based on rules with lexico-syntactic patterns representing linguistic information about terms and typical contexts of their usage in Russian scientific and educational texts; simple occurrences statistics of terms is used as well.  Experimental evaluation of the method has shown a considerable increase of precision and recall of term extraction compared with the widely-used standard techniques.

Language: English
Text on another site
Keywords: компьютерная лингвистикаcomputational linguisticsautomatic term extractionававтоматическое извлечение терминов

In book

Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 30 мая — 2 июня 2018 г.)
Вып. 17(24). , М.: Издательский центр «Российский государственный гуманитарный университет», 2018.
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