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News
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Проблемы обработки естественного языка в диалоговых системах

Системный администратор. 2019. № 10. С. 82–91.
Klyshinskiy E., Жеребцова Ю., Чижик А.

Nowadays, a field of dialogue systems and conversational agents is one of the rapidly growing research areas in artificial intelligence applications. Business and industry are showing increasing interest in implementing intelligent conversational agents into their products. Many recent studies has tended to focus on possibility of developing task-oriented systems which are able to have long and free social chats that occur naturally in social human interactions. In order to better understand the user’s expression, and then feedback the correct information, natural language understanding plays an extremely important role. Despite progress made in solving NLP problems, it remains very challenging today in the field of dialogue systems. In this paper, we review the recent progress in developing dialogue systems, its current architecture features and further prospects. We focus on the natural language understanding tasks which are key for building a good conversational agent, and than we are summarizing NLP methods and frameworks, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. Additionally, we consider the dialogue concept in context of human-machine interaction, and briefly describe dialogue evaluation metrics.

Research target: Mathematics Computer Science Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: Russian
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Keywords: искусственный интеллекткомпьютерная лингвистикаnatural language processingcomputational linguisticsобработка естественного языкаинтеллектуальный агентnatural language interfaceчат-ботдиалоговая системаNatural Language Understandingdialogue systemsconversational agentintelligent personal assistant
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