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  • Взiaлъ, възялъ, вьзял: Обработка орфографической вариативности при лексико-грамматической аннотации старорусского корпуса XV-XVII вв.
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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
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.

 

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Взiaлъ, възялъ, вьзял: Обработка орфографической вариативности при лексико-грамматической аннотации старорусского корпуса XV-XVII вв.

Вестник Православного Свято-Тихоновского гуманитарного университета. Серия 3: Филология. 2017. Т. 51. С. 11–20.
Гаврилова Т. С., Шалганова Т. А., Lyashevskaya O.

 

The highly unstable orthography of the Middle Russian texts poses a challenge for their automatic processing. The Middle Russian subcorpus of the Russian National Corpus (RNC) includes documents written mainly between 1400 and 1700, when the variation in spelling was still a norm. The task of lexico-grammatical analysis is to assign a dictionary form (lemma), part of speech and grammatical tags to each word form in the corpus. Traditional methods of pos- and grammatical tagging assume that there can be (almost only) one possible string of characters representing the stem and ending of each grammatical form of the word. Since unstable orthography yields many-to-many mapping between word forms and grammatical annotations, morphological taggers perform poorly and need orthographic normalization preprocessing.

We use both relative and absolute normalization of orthographic representation. The relative normalization involves multiplying orthographic representations of stems and endings in the grammatical dictionary by regular rules. It is carried out at the level of (a) word endings; (b) nominative stems with regular variation, e.g. russk(ij) / russt(ij), keli(ja) / kel'(ja); (c) nominative stems of the Church Slavonic origin, e.g. odin- / edin-; (d) verb stems with prefixes; etc. The absolute normalization matches characters (character combinations) which alternate regularly in the corpus (e.g. o / ѡ 'omega', e / ѣ, шт / щ, жю / жу). The absolute normalization applies to both orthographic representations in the grammatical dictionary and word forms in the text.

Research target: Philology and Linguistics
Priority areas: humanitarian
Language: Russian
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Keywords: Национальный корпус русского языкадревнерусский языкRussian National Corpusлексико-грамматическая разметкаOld Russian Languageорфографическая вариативностьmorphological analysisMiddle Russianlexico-grammatical taggingspelling variationстарорусская письменностьorthographic normalizationhistorical corpus linguisticsисторические корпусаорфовариантунификация орфографии при автоматической обработке текста
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