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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)

Issue 18. M. : Russian State University for the Humanitie, 2019.
Under the general editorship: Селегей В.

The book includes 64 papers submitted to the International conference in computer linguistics and intellectual technologies Dialogue 2019 and presents a broad spectrum of theoretical and applied research of natural language description, language simulation, and creation of applied computer technologies.

Chapters
Classification Models for RST Discourse Parsing of Texts In Russian
Chistova E., Shelmanov A., Kobozeva M. et al., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 163–176.
The paper considers the task of automatic discourse parsing of texts in Russian. Discourse parsing is a well-known approach to capturing text semantics across boundaries of single sentences. Discourse annotation was found to be useful for various tasks including summarization, sentiment analysis, question-answering. Recently, the release of manually annotated Ru-RSTreebank corpus unlocked the possibility of ...
Added: October 16, 2019
SOME FEATURES OF THE COMPLETIVE PREFIX DO- IN RUSSIAN: THEORY FACES EMPIRICAL DATA
Stoynova N., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 667–676.
Added: October 25, 2019
The Corpus of Contact-Influenced Russian of Northern Siberia and the Russian Far East
Khomchenkova I. A., Pleshak P. S., Stoynova N., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 276–287.
The paper presents a spoken corpus of contact-influenced Russian, which consists of oral spontaneous Russian speech of bilingual speakers of indigenous languages of Northern Siberia and the Russian Far East (Samoyedic, Tungusic, Chukotko-Kamchatkan). The texts included in the corpus were transcribed in ELAN in Standard Russian orthography and provided with a special system of manual annotation of contact-induced features developed ...
Added: October 25, 2019
Comparing models of morpheme analysis for Russian words based on machine learning
Bolshakova E. I., Sapin A., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 104–113.
The paper reports on the experimental comparison of several machine learning models proposed in recent years for automatic morpheme segmentation of Russian words, including conditional random fields (CRF), sequence-to-sequence neural network (Seq2seq) , convolutional neural network (CNN) model, as well as a new model we have developed with the aid of gradient boosted decision trees ...
Added: November 3, 2019
Contrast and comparison relations in RST framework
Toldova S., Davydova T., Kobozeva M. et al., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 714–727.
The paper is devoted to a corpus study of the Contrast relation between discourse units in Russian. It is based on the data of the Ru-RSTreebank annotated within the framework of the Rhetorical Structure theory [Mann, Thompson 1988]. The research question is what cue phrases and lexical and grammatical patterns are used to express the ...
Added: April 22, 2020
Gapping parsing using pretrained embeddings, attention mechanism and NCRF
Emelyanov A., Artemova E., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019. P. 203–212.
The article is devoted to the problem of automatic gapping resolution for the Russian language. We use BERT Language Model as embeddings with bidirectional recurrent net- work, attention, and NCRF on the top. Unlike other models these are using BERT, we apply BERT only as embedder without any fine-tuning. As a result, our implementation took ...
Added: October 29, 2020
An anaphora resolution system for Russian based on ETAP-4 linguistic processor
Inshakova E.S., , in: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)Issue 18.: M.: Russian State University for the Humanitie, 2019.
Added: April 10, 2025
Research target: Computer Science Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Text on another site
Keywords: русский языккомпьютерная лингвистикаnatural language processingкорпусная лингвистикаRussian languagecorpus linguisticsавтоматическая обработка текстаcomputational linguisticsинтеллектуальные технологииintellectual technologies
Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2019)
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