• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Laboratory for Models and Methods of Computational Pragmatics

Publications
Article
Data-driven models and computational tools for neurolinguistics: a language technology perspective

Ekaterina Artemova, Bakarov A., Artemov A. et al.

Journal of Cognitive Science. 2020. Vol. 1. No. 21. P. 15-53.

Book chapter
RENERSANs: Relation Extraction and Named Entity Recognition as Sequence Annotation

Davletov A., Gordeev D., Rei A. et al.

In bk.: Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue” (2020). 2020. P. 172-182.

Working paper
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing

Klyuchnikov N., Trofimov I., Artemova E. et al.

arXiv:[cs.LG]. arXiv:[cs.LG]. arXiv, 2020

About Laboratory

The  area of expertise of Computational Pragmatics 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.

News

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.

More news