1 code implementation • 23 Oct 2022 • Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin, Alena Pestova, Ivan Smurov, Ekaterina Artemova
Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.
Ranked #2 on Linguistic Acceptability on ItaCoLA
1 code implementation • 3 Jun 2022 • Tatiana Shamardina, Vladislav Mikhailov, Daniil Chernianskii, Alena Fenogenova, Marat Saidov, Anastasiya Valeeva, Tatiana Shavrina, Ivan Smurov, Elena Tutubalina, Ekaterina Artemova
The first task is framed as a binary classification problem.
1 code implementation • 3 May 2021 • Ilya Gusev, Ivan Smurov
The presented datasets for event detection and headline selection are the first public Russian datasets for their tasks.
no code implementations • 29 Oct 2020 • Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura, Vladimir Ivanov, Veronika Sarkisyan, Elena Tutubalina, Ivan Smurov
We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency.
1 code implementation • SEMEVAL 2020 • Ilya Dimov, Vladislav Korzun, Ivan Smurov
This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles.
1 code implementation • 1 Jul 2020 • Ekaterina Artemova, Tatiana Batura, Anna Golenkovskaya, Vitaly Ivanin, Vladimir Ivanov, Veronika Sarkisyan, Ivan Smurov, Elena Tutubalina
In this paper we present a corpus of Russian strategic planning documents, RuREBus.
1 code implementation • Proceedings of the International Conference “Dialogue 2020” 2020 • Alexey Sorokin, Ivan Smurov, Denis Kirianov
In this paper we describe our submission to GramEval2020 competition on morphological tagging, lemmatization and dependency parsing.
no code implementations • WS 2019 • Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina
This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7. 5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts.
no code implementations • 10 Jun 2019 • Maria Ponomareva, Kira Droganova, Ivan Smurov, Tatiana Shavrina
This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7. 5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts.