no code implementations • NAACL (SMM4H) 2021 • Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre, Salvador Lima López, Ivan Flores, Karen O’Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health.
no code implementations • NAACL (SMM4H) 2021 • Andrey Sakhovskiy, Zulfat Miftahutdinov, Elena Tutubalina
This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task.
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.
no code implementations • COLING (TextGraphs) 2022 • Irina Nikishina, Alsu Vakhitova, Elena Tutubalina, Alexander Panchenko
We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy.
1 code implementation • LREC 2022 • Natalia Loukachevitch, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Suresh Manandhar, Artem Shelmanov, Elena Tutubalina
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
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 • SMM4H (COLING) 2022 • Vera Davydova, Elena Tutubalina
This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates.
no code implementations • SMM4H (COLING) 2020 • Ari Klein, Ilseyar Alimova, Ivan Flores, Arjun Magge, Zulfat Miftahutdinov, Anne-Lyse Minard, Karen O’Connor, Abeed Sarker, Elena Tutubalina, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
no code implementations • SMM4H (COLING) 2022 • Davy Weissenbacher, Juan Banda, Vera Davydova, Darryl Estrada Zavala, Luis Gasco Sánchez, Yao Ge, Yuting Guo, Ari Klein, Martin Krallinger, Mathias Leddin, Arjun Magge, Raul Rodriguez-Esteban, Abeed Sarker, Lucia Schmidt, Elena Tutubalina, Graciela Gonzalez-Hernandez
For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content.
no code implementations • CLIB 2020 • Ilseyar Alimova, Elena Tutubalina, Alexander Kirillovich
As source data for transfer learning, we experimented with the full version of FrameNet and the reduced dataset with a smaller number of semantic roles identical to FrameBank.
1 code implementation • SMM4H (COLING) 2020 • Zulfat Miftahutdinov, Andrey Sakhovskiy, Elena Tutubalina
The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58% on English tweets and 51% on Russian tweets.
1 code implementation • 21 Nov 2023 • Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, Alán Aspuru-Guzik, Alex Zhavoronkov
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions.
1 code implementation • 14 Nov 2023 • Vera Davydova, Huabin Yang, Elena Tutubalina
The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination.
1 code implementation • 5 Nov 2023 • Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov, Alexander Telepov, Egor Rumiantsev, Alexey Skrynnik, Aleksandr I. Panov, Dmitry Vetrov, Elena Tutubalina, Artur Kadurin
Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules using $50$x less additional data.
1 code implementation • 21 Oct 2022 • Natalia Loukachevitch, Suresh Manandhar, Elina Baral, Igor Rozhkov, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Elena Tutubalina
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
1 code implementation • 21 Oct 2022 • Andrey Sakhovskiy, Elena Tutubalina
These components are state-of-the-art BERT-based models for language understanding and molecular property prediction.
1 code implementation • 11 Oct 2022 • Mark Rofin, Vladislav Mikhailov, Mikhail Florinskiy, Andrey Kravchenko, Elena Tutubalina, Tatiana Shavrina, Daniel Karabekyan, Ekaterina Artemova
The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives.
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
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 • 23 May 2022 • Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch, Igor Rozhkov, Tatiana Batura, Vladimir Ivanov, Elena Tutubalina
In the test set the frequency of all entity types is even.
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 • 24 Nov 2021 • Ilseyar Alimova, Elena Tutubalina
Automatic monitoring of adverse drug events (ADEs) or reactions (ADRs) is currently receiving significant attention from the biomedical community.
1 code implementation • 22 Nov 2021 • Daria Bakshandaeva, Denis Dimitrov, Vladimir Arkhipkin, Alex Shonenkov, Mark Potanin, Denis Karachev, Andrey Kuznetsov, Anton Voronov, Vera Davydova, Elena Tutubalina, Aleksandr Petiushko
Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language.
1 code implementation • RANLP 2021 • Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Ilia Denisov, Vladimir Ivanov, Suresh Manandhar, Alexander Pugachev, Elena Tutubalina
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
1 code implementation • 22 Jan 2021 • Zulfat Miftahutdinov, Artur Kadurin, Roman Kudrin, Elena Tutubalina
We investigate the effectiveness of transferring concept normalization from the general biomedical domain to the clinical trials domain in a zero-shot setting with an absence of labeled data.
1 code implementation • COLING 2020 • Elena Tutubalina, Artur Kadurin, Zulfat Miftahutdinov
Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation.
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 • 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 • 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.
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.
1 code implementation • 7 Apr 2020 • Juan M. Banda, Ramya Tekumalla, Guanyu Wang, Jingyuan Yu, Tuo Liu, Yuning Ding, Katya Artemova, Elena Tutubalina, Gerardo Chowell
As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research.
1 code implementation • 7 Apr 2020 • Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov, Andrey Sakhovskiy, Valentin Malykh, Sergey Nikolenko
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
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 #2 on Recommendation Systems on Netflix
no code implementations • RANLP 2019 • Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina
News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation.
no code implementations • 16 Aug 2019 • Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov, Eugene Beloded
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.
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 • WS 2019 • Ilseyar Alimova, Elena Tutubalina
This paper presents our experimental work on exploring the potential of neural network models developed for aspect-based sentiment analysis for entity-level adverse drug reaction (ADR) classification.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • WS 2019 • Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina
The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43. 2{\%}.
no code implementations • ACL 2019 • Zulfat Miftahutdinov, Elena Tutubalina
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
no code implementations • ACL 2019 • Ilseyar Alimova, Elena Tutubalina
Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology.
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.
no code implementations • 28 Nov 2018 • Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
no code implementations • 4 Dec 2017 • Elena Tutubalina, Zulfat Miftahutdinov
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest.