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Regular version of the site

Laboratory for Models and Methods of Computational Pragmatics

Publications
Article
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews

Тутубалина Е. В., Nikolenko S. I., Алимова И. С. et al.

Bioinformatics. 2020. Vol. 7. P. 1-7.

Book chapter
Deep Learning for the Russian Language

Artemova E.

In bk.: The Palgrave Handbook of Digital Russia Studies. Palgrave Macmillan, 2021. Ch. 26. P. 465-481.

Working paper
Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data In press

Ignatov D. I., Khvorykh G. V., Khrunin A. V. et al.

Lecture Notes in Computer Science. LNCS. Springer, 2020

About the Laboratory

The area of expertise of Lab is unstructured data analysis. We study recommending systems and services, develop methods for multimodal clustering and classification that allow profiling user interests based on various modalities. We do not treat data mining and machine learning models as black boxes and focus on developing interpretable algorithms.
We work in natural language processing (NLP). The focus of research lies in such areas as question-answering and information extraction. The Laboratory examines learning methods, in particular, transfer learning and domain adaptation techniques in multilingual settings, and applies these methods in practice. The Laboratory advances digital Russian studies by creating new annotated data sources, that represent society changes and such complex phenomena as education and economy digitalization.


Our Lab held the RuREBus shared task

More information about the corpus, types of relations and entities can be found in the repository of competition, which we held at the Dialog 2020 conference on RuREBus data.