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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Anaphoric annotation and corpus-based anaphora resolution: An experiment

Компьютерная лингвистика и интеллектуальные технологии. 2014. P. 562–571.
Alexeeva S. V., Protopopova E. V., Bodrova A. A., Volskaya S. A., Krylova I. V., Chuchunkov A. S., Granovsky D. V., Bocharov V. V.

The paper describes the noun phase and anaphora annotation in OpenCorpora and compares it to that in other corpora. We discuss the choice of representative texts for anaphoric annotation and the basic principles of syntactic annotation. In case of noun phrase annotation we followed the scheme introduced earlier for morphological annotation: it was carried out in two stages: firstly, all noun phrases and some other syntactic units were annotated by a heterogenous group of people, then a linguist compared all markup results and found the best one, or corrected mistakes. We present some annotation results and cases of annotator's disagreement and proceed to introduce our data-driven anaphora resolution system based on decision trees. We then list the features used to fit the classificator and discuss their relevance and some changes which improved the classificator performance. We also present out rule-based approach to automated noun phrase extraction using Tomita parser. A baseline for anaphora resolution is introduced and we compare it with our results.

Research target: Computer Science Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Text on another site
Keywords: crowdsourcingкраудсорсингкорпусанафораcorpora linguisticsanaphora resolutionSyntactic annotationсинтаксическая разметка
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