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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.
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Lexicographic Study of Synonymy: Clarifying Semantic Similarity between Words

Computacion y Sistemas. 2021. Vol. 25. No. 3. P. 667–675.
Solovyev V., Гималетдинова Г., Халитова Л., Бочкарев В.

The problem of determining semantic similarity between words affects the understanding of synonymy 13 and creates obstacles to the work of lexicographers. The study was carried out as a part of a larger 14 research project on expert assessment of synonymic rows in RuWordNet thesaurus (a WordNet–like 15 thesaurus for the Russian language). The aim of this study is to analyze RuWordNet thesaurus 16 and compare it with classical dictionaries of Russian synonyms. For this purpose, the authors 17 singled out entry words (adjectives N = 68 and verbs N = 117) and their analogues (adjectives 18 N = 558 and verbs N = 1410) from the New Explanatory Dictionary of Russian Synonyms by 19 Yu. Apresyan (NEDS). An analogue is viewed as a word whose meaning essentially intersects with 20 the general meaning of a given synonymic row, although it lacks the needed semantic similarity 21 that could indicate the presence of synonymy or near–synonymy (Apresyan). The quantitative 22 analysis based on the breadth–first search (BFS) algorithm estimated the distance between each pair 23 entry word→analogue. The quantitative method revealed that the analogues described in NEDS 24 correlate with the hyponyms and hyperonyms in RuWordNet which contributes to the study of 25 near–synonymy. 26 The qualitative method (observation and linguistic interpretation) was used to analyze pairs 27 entry word→analogue which showed the longest distance; such words were 52 adjectives and 15 28 verbs. First, the meanings of entry words and analogues were checked against two Russian language 29 thesauri, then, their representation in the tree graph of RuWordNet was traced. The analysis 30 revealed inaccuracies concerning the similarity between certain words. The recommendations for 31 further improvement of RuWordNet were given.

Research target: Philology and Linguistics
Language: English
Full text
Keywords: синонимыsynonymysemantic similarityRuWordNetsnear–synonymсемантическая похожестьпочти синонимыRuWordNet
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