1 code implementation • LREC 2022 • Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
Medical data annotation requires highly qualified expertise.
no code implementations • Findings (ACL) 2022 • Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
1 code implementation • 17 Dec 2024 • Anton Alekseev, Alina Tillabaeva, Gulnara Dzh. Kabaeva, Sergey I. Nikolenko
The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora.
2 code implementations • 16 Nov 2024 • Anton Alekseev, Gulnara Kabaeva
One of the key tasks in modern applied computational linguistics is constructing word vector representations (word embeddings), which are widely used to address natural language processing tasks such as sentiment analysis, information extraction, and more.
no code implementations • 8 Nov 2024 • Anton Alekseev, Timur Turatali
Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging.
1 code implementation • 30 Sep 2024 • Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, Andrey V. Savchenko, Sergey Nikolenko
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.
1 code implementation • 20 Jun 2024 • Kuzma Khrabrov, Anton Ber, Artem Tsypin, Konstantin Ushenin, Egor Rumiantsev, Alexander Telepov, Dmitry Protasov, Ilya Shenbin, Anton Alekseev, Mikhail Shirokikh, Sergey Nikolenko, Elena Tutubalina, Artur Kadurin
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science.
1 code implementation • 30 Aug 2023 • Anton Alekseev, Sergey I. Nikolenko, Gulnara Kabaeva
Kyrgyz is a very underrepresented language in terms of modern natural language processing resources.
no code implementations • 18 Jul 2023 • Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.
1 code implementation • 24 Jun 2022 • Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, Sergey Nikolenko
Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.
Ranked #1 on
Open Information Extraction
on LSOIE
no code implementations • 25 Nov 2021 • Anton Alekseev, Elena Tutubalina, Sejeong Kwon, Sergey Nikolenko
In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.
no code implementations • COLING 2020 • Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, Sergey Nikolenko
Understanding image advertisements is a challenging task, often requiring non-literal interpretation.
no code implementations • 17 Jun 2020 • Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
3 code implementations • 24 Dec 2019 • Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey I. Nikolenko
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(Recall@50 metric)
no code implementations • WS 2019 • Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
no code implementations • 23 Jan 2019 • Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh, Ilya Shenbin, Anton Alekseev
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.