Elena Tutubalina
Cited by
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A large-scale COVID-19 Twitter chatter dataset for open scientific research--an international collaboration
JM Banda, R Tekumalla, G Wang, J Yu, T Liu, Y Ding, E Artemova, ...
arXiv preprint arXiv:2004.03688, 2020
SentiRuEval: testing object-oriented sentiment analysis systems in Russian
N Loukachevitch, P Blinov, E Kotelnikov, Y Rubtsova, V Ivanov, ...
Proceedings of International Conference Dialog 2, 3-13, 2015
Medical concept normalization in social media posts with recurrent neural networks
E Tutubalina, Z Miftahutdinov, S Nikolenko, V Malykh
Journal of biomedical informatics 84, 93-102, 2018
Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews
E Tutubalina, S Nikolenko
Journal of Healthcare Engineering, 1-9, 2017
Recvae: A new variational autoencoder for top-n recommendations with implicit feedback
I Shenbin, A Alekseev, E Tutubalina, V Malykh, SI Nikolenko
Proceedings of the 13th International Conference on Web Search and Data, 2020
Identifying disease-related expressions in reviews using conditional random fields
Z Miftahutdinov, A Tropsha, E Tutubalina
Computational Linguistics and Intellectual Technologies: Papers from the, 2017
Overview of the fifth social media mining for health applications (# smm4h) shared tasks at coling 2020
A Klein, I Alimova, I Flores, A Magge, Z Miftahutdinov, AL Minard, ...
Proceedings of the Fifth Social Media Mining for Health Applications, 2020
Automated detection of adverse drug reactions from social media posts with machine learning
I Alimova, E Tutubalina
International Conference on Analysis of Images, Social Networks and Texts, 3-15, 2017
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
Z Miftahutdinov, E Tutubalina
Proceedings of the 57th Conference of the Association for Computational, 2019
Kfu at clef ehealth 2017 task 1: Icd-10 coding of english death certificates with recurrent neural networks
Z Miftakhutdinov, E Tutubalina
CEUR Workshop Proceedings, 2017
Overview of the sixth social media mining for health applications (# smm4h) shared tasks at naacl 2021
A Magge, A Klein, A Miranda-Escalada, MA Al-Garadi, I Alimova, ...
Proceedings of the Sixth Social Media Mining for Health (# SMM4H) Workshop, 2021
Extracting aspects, sentiment and categories of aspects in user reviews about restaurants and cars
VV Ivanov, EV Tutubalina, NR Mingazov, IS Alimova
Komp'juternaja Lingvistika i Intellektual'nye Tehnologii, 22-33, 2015
KFU NLP team at SMM4H 2019 tasks: Want to extract adverse drugs reactions from tweets? BERT to the rescue
Z Miftahutdinov, I Alimova, E Tutubalina
Proceedings of the fourth social media mining for health applications, 2019
Multiple features for clinical relation extraction: a machine learning approach
I Alimova, E Tutubalina
Journal of biomedical informatics 103, 103382, 2020
Exploring convolutional neural networks and topic models for user profiling from drug reviews
E Tutubalina, S Nikolenko
Multimedia Tools and Applications 77 (4), 4791-4809, 2018
Inferring sentiment-based priors in topic models
E Tutubalina, S Nikolenko
Mexican International Conference on Artificial Intelligence, 92-104, 2015
Rurebus-2020 shared task: Russian relation extraction for business
VA Ivanin, EL Artemova, TV Batura, VV Ivanov, VV Sarkisyan, ...
Computational Linguistics and Intellectual Technologies, 416-431, 2020
Distant supervision for sentiment attitude extraction
N Rusnachenko, N Loukachevitch, E Tutubalina
Proceedings of the International Conference on Recent Advances in Natural, 2019
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
E Tutubalina, I Alimova, Z Miftahutdinov, A Sakhovskiy, V Malykh, ...
Bioinformatics, 2020
Using semantic analysis of texts for the identification of drugs with similar therapeutic effects
EV Tutubalina, ZS Miftahutdinov, RI Nugmanov, TI Madzhidov, ...
Russian Chemical Bulletin 66 (11), 2180-2189, 2017
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