144 citations · 463 across the 32 of their papers we have counts for
37 papers
RuArg-2022: Argument Mining Evaluation
Evgeny Kotelnikov, Natalia Loukachevitch, Irina Nikishina +1
Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argu…
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy +1
Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a bac…
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova +7
Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with acti…
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina, Alexander Panchenko, Varvara Logacheva +1
Ontologies, taxonomies, and thesauri are used in many NLP tasks. However, most studies are focused on the creation of these lexical resources rather than the maintenance of the exi…
Neural Entity Linking: A Survey of Models Based on Deep Learning
Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov +2
This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language…
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy +1
Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extra…