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Model trained to recognize 4 types of relationships between significant pharmacological entities in russian-language reviews: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. The input of the model is a review text and a pair of entities, between which it is required to determine the fact of a relationship and one of the 4 types of relationship, listed above.


Proposed model is trained on a subset of 908 reviews of the Russian Drug Review Corpus (RDRS). The subset contains the pairs of entities marked with the 4 listed types of relationships:

  • ADR-Drugname — the relationship between the drug and its side effects
  • Drugname-SourceInfodrug — the relationship between the medication and the source of information about it (e.g., “was advised at the pharmacy”, e.g., “was advised at the pharmacy”, “the doctor recommended it”);
  • Drugname-Diseasname — the relationship between the drug and the disease
  • Diseasename-Indication — the connection between the illness and its symptoms (e.g., “cough”, “fever 39 degrees”) Also, this subset contains pairs of the same entity types between which there is no relationship: for example, a drug and an unrelated side effect that appeared after taking another drug; in other words, this side effect is related to another drug.

Model topology and training

Proposed model is based on the XLM-RoBERTA-large topology. After the additional training as a language model on corpus of unmarked drug reviews, this model was trained as a classification model on 80% of the texts from subset of the corps described above.

How to use

See section "How to use" in our git repository for the model


Here are the accuracy, estimated by the f1 score metric for the recognition of relationships on the best fold.

ADR–Drugname Drugname–Diseasename Drugname–SourceInfoDrug Diseasename–Indication
0.955 0.892 0.922 0.891

Citation info

If you have found our results helpful in your work, feel free to cite our publication as:

  title={Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications},
  author={Sboev, Alexander and Selivanov, Anton and Moloshnikov, Ivan and Rybka, Roman and Gryaznov, Artem and Sboeva, Sanna and Rylkov, Gleb},
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