# The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews

arXiv: https://arxiv.org/abs/2004.03659 · HTML: https://mlsanity.com/paper/2004.03659/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 11 of 11)

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- [AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models](https://mlsanity.com/paper/2105.00827/) (2021)
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- [RuMedBench: A Russian Medical Language Understanding Benchmark](https://mlsanity.com/paper/2201.06499/) (2022)
- [NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities](https://mlsanity.com/paper/2210.11913/) (2022)
- [Review of Natural Language Processing in Pharmacology](https://mlsanity.com/paper/2208.10228/) (2022)
- [Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction Classification](https://mlsanity.com/paper/2210.13238/) (2022)
- [Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective](https://mlsanity.com/paper/2208.02031/) (2022)
- [An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets](https://mlsanity.com/paper/2105.00059/) (2021)
- [A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages](https://mlsanity.com/paper/2403.18336/) (2024)
- [BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition](https://mlsanity.com/paper/2604.02904/) (2026)

## Abstract (arXiv)

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 entities and the effectiveness of pharmaceutical products. The corpus itself consists of two parts, the raw one and the labelled one. The raw part includes 1.4 million health-related user-generated texts collected from various Internet sources, including social media. The labelled part contains 500 consumer reviews about drug therapy with drug- and disease-related information. Labels for sentences include health-related issues or their absence. The sentences with one are additionally labelled at the expression level for identification of fine-grained subtypes such as drug classes and drug forms, drug indications, and drug reactions. Further, we present a baseline model for named entity recognition (NER) and multi-label sentence classification tasks on this corpus. The macro F1 score of 74.85% in the NER task was achieved by our RuDR-BERT model. 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. We make the RuDReC corpus and pretrained weights of domain-specific BERT models freely available at https://github.com/cimm-kzn/RuDReC