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Found 55 papers, 27 papers with code
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
RuCCoN: Clinical Concept Normalization in Russian
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Medical data annotation requires highly qualified expertise.
Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022
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.
SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19
This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates.
Cross-Modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs
We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy.
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions
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.
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021
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.
Cross-lingual Transfer Learning for Semantic Role Labeling in Russian
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.
KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects
This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task.
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
This paper presents a system developed for SemEval 2025 Task 8: Question Answering (QA) over tabular data.
Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models
This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspectives (USA, UK, USSR, and China).
One Task Vector is not Enough: A Large-Scale Study for In-Context Learning
In-context learning (ICL) enables Large Language Models (LLMs) to adapt to new tasks using few examples, with task vectors - specific hidden state activations - hypothesized to encode task information.
Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models
The generation of realistic medical images from text descriptions has significant potential to address data scarcity challenges in healthcare AI while preserving patient privacy.
I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders
Large Language Models (LLMs) have achieved remarkable success in natural language processing.
RuCCoD: Towards Automated ICD Coding in Russian
This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources.
Confidence Estimation for Error Detection in Text-to-SQL Systems
Text-to-SQL enables users to interact with databases through natural language, simplifying the retrieval and synthesis of information.
CLEAR: Character Unlearning in Textual and Visual Modalities
Machine Unlearning (MU) is critical for enhancing privacy and security in deep learning models, particularly in large multimodal language models (MLLMs), by removing specific private or hazardous information.
nach0-pc: Multi-task Language Model with Molecular Point Cloud Encoder
Our approach utilizes a molecular point cloud encoder for concise and order-invariant structure representation.
$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science.
Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task
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.
Gradual Optimization Learning for Conformational Energy Minimization
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.
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification
These components are state-of-the-art BERT-based models for language understanding and molecular property prediction.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
Vote'n'Rank: Revision of Benchmarking with Social Choice Theory
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.
DetIE: Multilingual Open Information Extraction Inspired by Object Detection
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
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
The first task is framed as a binary classification problem.
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
In the test set the frequency of all entity types is even.
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
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.
Selection of pseudo-annotated data for adverse drug reaction classification across drug groups
Automatic monitoring of adverse drug events (ADEs) or reactions (ADRs) is currently receiving significant attention from the biomedical community.
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
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.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
Drug and Disease Interpretation Learning with Biomedical Entity Representation Transformer
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.
Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
Understanding image advertisements is a challenging task, often requiring non-literal interpretation.
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models
Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation.
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain
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.
So What's the Plan? Mining Strategic Planning Documents
In this paper we present a corpus of Russian strategic planning documents, RuREBus.
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
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
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.
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
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.
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
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)
Distant Supervision for Sentiment Attitude Extraction
News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation.
CommentsRadar: Dive into Unique Data on All Comments on the Web
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.
AspeRa: Aspect-Based Rating Prediction Based on User Reviews
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models
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.
KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue
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{\%}.
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
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).
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks
Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology.
AspeRa: Aspect-based Rating Prediction Model
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.
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts
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).
An Encoder-Decoder Model for ICD-10 Coding of Death Certificates
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest.
