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News
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.
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.

 

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Text collections for evaluation of Russian morphological taggers

Jazykovedny Casopis. 2017. Vol. 68. No. 2. P. 258–267.
Lyashevskaya O., Bocharov V., Sorokin A., Shavrina T., Granovsky D., Alexeeva S.

The paper describes the preparation and development of the text collections within the framework of MorphoRuEval-2017 shared task, an evaluation campaign designed to stimulate development of the automatic morphological processing technologies for Russian. The main challenge for the organizers was to standardize all available Russian corpora with the manually verified high-quality tagging to a single format (Universal Dependencies CONLL-U). The sources of the data were the disambiguated subcorpus of the Russian National Corpus, SynTagRus, OpenCorpora.org data and GICR corpus with the resolved homonymy, all exhibiting different tagsets, rules for lemmatization, pipeline architecture, technical solutions and error systematicity. The collections includes both normative texts (the news and modern literature) and more informal discourse (social media and spoken data), the texts are available under CC BY-NC-SA 3.0 license.

Research target: Computer Science Philology and Linguistics
Priority areas: humanitarian
Language: English
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DOI
Text on another site
Keywords: natural language processingлексико-грамматическая разметкакорпуса языкаmorphological tagginguniversal dependenciesуниверсальные зависимостиtext collectionshared taskRussian corporaтекстовые коллекцииоценка автоматических методов обработки текста
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