311 citations · 752 across the 16 of their papers we have counts for
17 papers
nach0: Multimodal Natural and Chemical Languages Foundation Model
Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina +8
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creat…
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
Andrey Sakhovskiy, Elena Tutubalina
In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text…
Vote'n'Rank: Revision of Benchmarking with Social Choice Theory
Mark Rofin, Vladislav Mikhailov, Mikhail Florinskiy +5
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…
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch +4
The RuNNE Shared Task approaches the problem of nested named entity recognition. The annotation schema is designed in such a way, that an entity may partially overlap or even be ne…
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura +6
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it co…
Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer
Zulfat Miftahutdinov, Artur Kadurin, Roman Kudrin +1
Concept normalization in free-form texts is a crucial step in every text-mining pipeline. Neural architectures based on Bidirectional Encoder Representations from Transformers (BER…
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura +4
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, issue…
So What's the Plan? Mining Strategic Planning Documents
Ekaterina Artemova, Tatiana Batura, Anna Golenkovskaya +5
In this paper we present a corpus of Russian strategic planning documents, RuREBus. This project is grounded both from language technology and e-government perspectives. Not only n…
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Anton Alekseev, Elena Tutubalina, Valentin Malykh +1
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.…
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov +3
The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named…
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
Juan M. Banda, Ramya Tekumalla, Guanyu Wang +6
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. The unparalleled r…
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Ilya Shenbin, Anton Alekseev, Elena Tutubalina +2
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which…
CommentsRadar: Dive into Unique Data on All Comments on the Web
Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov +1
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites. The engin…
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
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 v…
AspeRa: Aspect-based Rating Prediction Model
Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh +2
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 a…
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko +1
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, usual…
An Encoder-Decoder Model for ICD-10 Coding of Death Certificates
Elena Tutubalina, Zulfat Miftahutdinov
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest. The task of medical concept coding…