15 citations · 42 across the 11 of their papers we have counts for
23 papers · 1 filter
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…
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…
RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
Irina Nikishina, Varvara Logacheva, Alexander Panchenko +1
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previousl…
Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva, Denis Teslenko, Artem Shelmanov +7
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed t…
Making Fast Graph-based Algorithms with Graph Metric Embeddings
Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk +2
The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs. We explore dense vector representations as an effective way to appro…