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README.md

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+Relation_Extraction
+===================
+This repository provides code and additional materials of the paper: "Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications".
+
+In this work, we trained a model to recognize 4 types of relationships between entities in drug review texts: 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.
+
+Data
+----
+Proposed model is trained on a subset of 908 reviews of the [Russian Drug Review Corpus (RDRS)](https://arxiv.org/pdf/2105.00059.pdf). The subset contains the markup of the following types of entities: and contains 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 the187source of information about it (e.g., “was advised at the pharmacy”, “the -Drugname-- - Drugname-Diseasename — 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
+----
+Proposed model is based on the  [XLM-RoBERTA-large](https://arxiv.org/abs/1911.02116) topology. After the additional training as a langauge 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. This model showed the best accuracy on one of the folds of the cross-validation. For additional details see original paper.
+
+How to use
+----
+See section "How to use" in [our git repository for the model](https://github.com/sag111/Relation_Extraction)
+
+Results
+----
+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                  |