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Found 10 papers, 2 papers with code
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.
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.
RuBia: A Russian Language Bias Detection Dataset
To illustrate the dataset's purpose, we conduct a diagnostic evaluation of state-of-the-art or near-state-of-the-art LLMs and discuss the LLMs' predisposition to social biases.
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
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.
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.
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{\%}.
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.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks
Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology.
