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News
July 2, 2026
Researchers Discover How Spelling Errors Slow Down Reading in Russian
Psycholinguists from the Centre for Language and Brain at HSE University–St Petersburg have shown that words that are frequently misspelled are processed more slowly by readers, even when presented with the correct spelling. The researchers confirmed this effect for the first time using Russian-language materials and found that response speed is most strongly linked to how confidently individuals can distinguish the correct spelling of a word from an incorrect one. The study has been published in The Mental Lexicon.
July 2, 2026
HSE Develops App for Assessing Phonological Processing in Children
Researchers at the HSE Centre for Language and Brain have developed a new digital tool for assessing children's phonological processing skills—the ZARYA (Sound Analysis of the Russian Language) test battery. It is the first standardised application in Russia designed to provide a fast and reliable assessment of children's ability to distinguish speech sounds, retain them in working memory, and perform phonemic analysis. The app runs on Android tablets and smartphones and is available for download from RuStore. Details of the test validation have been published in the Journal of Speech, Language, and Hearing Research.
July 1, 2026
Scientists Discover Why Europium 'Misbehaves'
Europium is a rare-earth metal responsible for the pure red glow in displays and other luminescent materials. For a long time, however, it refused to emit light when surrounded by certain organic molecules known as acylpyrazolone ligands. Chemists have now uncovered the reason: in europium complexes with these ligands, a 'black window' appears—a charge-transfer state in which the energy absorbed by the ligand is dissipated as heat rather than emitted as light. Understanding this mechanism opens the way to designing more efficient red-emitting materials for displays, fluorescent thermometers, and chemical sensors. The results have been published in Dalton Transactions.

 

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Some Features of Sentiment Analysis for Russian Language Posts and Comments from Social Networks

Journal of Physics: Conference Series. 2021. Vol. 1740. P. 1–6.
Sidorov Nikita, Slastnikov Sergey

Sentiment analysis of different language texts is one of the very popular machine learning tasks. The complexity of its solution depends both on the characteristics of a particular language, and on the length of the evaluated texts. In our work, we consider the task of creating a sentiment analysis software tool for Russian posts and comments from the most popular social networks without any domain restriction. The features of constructing both the algorithmic and the software parts of the problem are described, some quality and performance metrics of the suggested neural network system are presented.

Priority areas: IT and mathematics
Language: English
DOI
Keywords: embeddingconvolutional neural networksSentiment Analyses
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