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MorphoRuEval-2017: an Evaluation Track for the Automatic Morphological Analysis Methods for Russian
P. 297–313.
Sorokin A., Shavrina T., Lyashevskaya O., Дроганова К. А., Alexeeva S. V., Bocharov V., Fenogenova A., Granovsky D.
MorphoRuEval-2017 is an evaluation campaign designed to stimulate the development of the automatic morphological processing technologies for Russian, both for normative texts (news, fiction, nonfiction) and those of less formal nature (blogs and other social media). This article compares the methods participants used to solve the task of morphological analysis. It also discusses the problem of unification of various existing training collections for Russian language
In book
Vol. 1. Issue 16 (23). , M.: -, 2017.
П.Е. Белова, А.К. Сафарян, В кн.: Научно-практическая конференция с международным участием "Национальные и международные тенденции и перспективы развития судебной экспертизы". Сборник докладов.: Н. Новгород: Изд-во ННГУ им. Н.И. Лобачевского, 2024.
В данной статье представлено описание системы автоматического поиска и извлечения побуждений из текстов на русском языке FindImper, основанной на поиске глагольных форм и синтаксических связей. Алгоритм реализован на языке программирования Python с использованием библиотек для морфологического и синтаксического анализа и набора правил. Данный инструмент направлен на оптимизацию работы эксперта-лингвиста и доступен к использованию через веб-сайт ...
Added: January 30, 2026
Mylnikova A., Mylnikov L., Научно-техническая информация. Серия 2: Информационные процессы и системы 2025 № 7 С. 32–44
Рассмотрена модель использования скелетных структур на базе синтаксической разметки для предобработки корпусов текстов перед передачей в нейросетевые модели машинного перевода с целью повышения качества их работы, реализованная с помощью частеречной и синтаксической разметок корпусов текстов, использующих языковую модель, с использованием сети BERT и набора правил. Описана подготовка данных для обучения и предложены способы повышения эффективности ...
Added: September 22, 2025
Morozov D., Astapenka L., Glazkova A. et al., , in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Vol. 1: Long papers.: Association for Computational Linguistics, 2025. P. 6795–6815.
Automatic morpheme segmentation algorithms are applicable in various tasks, such as building tokenizers and language education. For Slavic languages, the development of such algorithms is complicated by the rich derivational capabilities of these languages. Previous research has shown that, on average, these algorithms have already reached expert-level quality. However, a key unresolved issue is the ...
Added: July 17, 2025
Белова П.Е., Safaryan A., В кн.: Современный медиатекст и судебная экспертиза: междисциплинарные связи и экспертная оценка: сборник научных работ по итогам Международной научно-практической конференции «Современный медиатекст и судебная экспертиза: междисциплинарные связи и экспертная оценка».: М.: ООО «СОЮЗКНИГ», 2023. С. 46–56.
В статье представлено описание системы автоматического поиска и извлечения побуждений из текстов на русском языке FindImper, основанной на поиске глагольных форм, выражающих значение побуждения, и реализованной на языке Python с использованием библиотек для морфологического и синтаксического анализа и набора правил. ...
Added: October 29, 2023
Koshevoy A., Panova A., Makarchuk I., , in: Proceedings of the Sixth Workshop on Universal Dependencies (UDW, GURT/SyntaxFest 2023).: Washington: Association for Computational Linguistics, 2023. P. 1–6.
In this paper, we discuss the challenges that we faced during the construction of a Universal Dependencies treebank for Abaza, a polysynthetic Northwest Caucasian language. We propose an alternative to the morpheme-level annotation of polysynthetic languages introduced in Park et al. (2021). Our approach aims at reducing the number of morphological features, yet providing all ...
Added: March 20, 2023
Washington: Association for Computational Linguistics, 2023.
Added: March 20, 2023
Yana Shishkina, Lyashevskaya O., , in: Analysis of Images, Social Networks and Texts. 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021, Revised Selected Papers.: Cham: Springer, 2022. P. 137–147.
Enhanced Universal Dependencies (EUD) are enhanced graphs expressed on top of basic dependency trees. EUD support repre- sentation of deeper syntactic relations in constructions such as coordi- nation, gapping, relative clauses, and argument sharing through control and raising. The paper presents experiments on the EUD parsing of the low-resource Belarusian language, for which no corpora ...
Added: January 4, 2022
Lyashevskaya O., Afanasev I., Jazykovedny Casopis 2021 Vol. 72 No. 2 P. 556–567
We present a hybrid HMM-based PoS tagger for Old Church Slavonic. The training corpus is a portion of one text, Codex Marianus (40k) annotated with the Universal Dependencies UPOS tags in the UD-PROIEL treebank. We perform a number of experiments in within-domain and out-of-domain settings, in which the remaining part of Codex Marianus serves as ...
Added: October 21, 2021
Moroz G., , in: Дурхъаси хазна. Сборник статей к 60-летию Р. О. Муталова.: М.: Буки Веди, 2021. P. 258–282.
In this article I present a connection between frequency and length of person-number indexes via two independent researches: token frequency obtained from the Universal Dependencies’ treebanks and type frequency gathered within a typological study. After introducing the results of those two studies, I will present East Caucasian data. I show that the unusual history of ...
Added: May 23, 2021
Lyukina E. V., Lytaeva M. A., Вестник Томского государственного университета. Филология 2020 № 68 С. 27–41
The article is dedicated to a new method for predicting the morphological paradigm of unknown (non-dictionary) words in the Russian language. The method allows in incremental mode automatically predict the morphological paradigm of the word. The method is based on ensemble prediction of the morphological paradigm from single wordform and the formation of partial paradigms ...
Added: December 11, 2020
Lyashevskaya O., Ostyakova L., Сальников Е. А. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17 июня — 20 июня 2020 г.). Дополнительный том материалов.: M.: ., 2020. P. 1091–1108.
Orthographic and morphological heterogeneity of historical texts in pre-modern Slavic causes many difficulties in pos- and morphological tagging. Existing approaches to these tasks show state-of-the-art results without normalization, but they are still very sensitive to the properties of training data such as genre and origin. In this paper, we investigate to what extent the heterogeneity ...
Added: July 3, 2020
Durandin O., Malafeev A., , in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information ScienceVol. 1086.: Springer, 2020. P. 120–131.
In recent works on learning representations for graph structures, methods have been proposed both for the representation of nodes and edges for large graphs, and for representation of graphs as a whole. This paper considers the popular graph2vec approach, which shows quite good results for ordinary graphs. In the field of natural language processing, however, ...
Added: November 16, 2019
Lyashevskaya O., , in: Computational Linguistics and Intellectual TechnologiesIssue 18.: M.: Russian State University for the Humanitie, 2019. P. 422–434.
The paper discusses the standardization efforts to create a morphological standard for the Middle Russian corpus, which is part of the historical collection of the Russian National Corpus (RNC). To meet the needs of different categories of corpus researchers as well as NLP developers, we consider two styles of the morphological annotation (RNC schema and ...
Added: June 12, 2019
Builova N., , in: Proceedings of Third Workshop "Computational linguistics and language science"Issue 4.: Manchester: EasyChair, 2019.
In our research we studied the dependency structure of the text genre love stories, detective stories, science fiction and fantasy). The novel characteristics (such syntactic attributes as verb constructions and construction of a specific cumulative threshold) which can be additional machine learning parameters were identified. We conducted experiment with novel features and showed that these ...
Added: December 11, 2018
Lyashevskaya O., Пантелеева И. М., , in: Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories (TLT 16).: Association for Computational Linguistics, 2017. P. 80–87.
The paper presents a Universal Dependencies (UD) annotation scheme for a learner English corpus. The REALEC dataset consists of essays written in English by Russian-speaking university students in the course of general English. The original corpus is manually annotated for learners’ errors and gives information on the error span, error type, and the possible correction ...
Added: December 11, 2018
Дроганова К. А., Lyashevskaya O., Zeman D., , in: Proceedings of TLT 2018 International Workshop on Treebanks and Linguistic Theories, 13-14 November 2018, Oslo, Norway. NEALT Proceedings Series.: Linköping University Electronic Press, 2018. P. 52–65.
In this paper we focus on syntactic annotation consistency within Universal Dependencies (UD) treebanks for Russian: UD_Russian-SynTagRus, UD_Russian-GSD, UD\_Russian-Taiga, and UD_Russian-PUD. We describe the four treebanks, their distinctive features and development. In order to test and improve consistency within the treebanks, we reconsidered the experiments by Martinez Alonso and Zeman; our parsing experiments were conducted ...
Added: November 6, 2018
Дроганова К. А., Lyashevskaya O., , in: Digital Transformation and Global Society Third International Conference, DTGS 2018, St. Petersburg, Russia, May 30 –June 2, 2018, Revised Selected Papers, Part IIssue 858.: Cham: Springer, 2018. Ch. 31 P. 380–390.
Cross-tagset parsing is based on the substitution of one annotation layer for another while processing data within one language. As often as not, either the native tagger or the dependency parser used in (pre-)annotation of the Gold treebank is not available. The crosstagset approach allows one to annotate new texts using freely available tools or ...
Added: October 10, 2018
Fenogenova A., Kazorin V., Karpov I. et al., , in: Proceedings of Third Workshop "Computational linguistics and language science"Issue 4.: Manchester: EasyChair, 2019. P. 11–17.
Automatic morphological analysis is one of the fundamental and significant tasks of NLP (Natural Language Processing). Due to special features of Internet texts, as they can be both normative texts (news, fiction, nonfiction) and less formal texts (such as blogs and texts from social networks), the morphological tagging has become non-trivial and an actual task. ...
Added: October 5, 2018