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October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.

 

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A hybrid lemmatiser for Old Church Slavonic

NRU HSE , 2021.
Afanasev I.
The article considers a lemmatiser that is developed specifically for Old Church Slavonic (OCS). The introduction underlines the problem of the lack of lemmatisers that might deal with different datasets of the OCS. The review gives a short description of previous attempts and current trends in lemmatisation. The lemmatiser is hybrid-based and uses the advantages of linguistic rules for specific cases (fragmentary tokens, punctuation, or digits), a dictionary for the most common tokens, and a sequence-to-sequence (seq2seq) neural network with an attention mechanism for the rest of material. The model achieves an 85% overall accuracy score, which is lower than one of the previous models for the Universal Dependencies(UD) dataset. However, when specific tokens are taken into consideration, the model outperforms the previous ones with the help of its rule-based part. Possible further directions of the research include the use of more sophisticated architectures, such as BART.
Research target: Philology and Linguistics
Priority areas: humanitarian
Language: English
Full text
Keywords: автоматическая обработка естественного языкалемматизациягибридный подходстарославянский языкhybrid approachOld Church SlavonicNeural Language Processing (NLP)lemmatizationseq2seqseq2seq
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