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Description
In this paper we estimate accuracy of solving the task of relation extraction from texts containing pharmacologically significant information on the set of corpora in two languages:
1) the expanded version of RDRS corpus, that contains texts of internet reviews on medications in Russian;
2) the DDI2013 dataset containing MEDLINE abstracts and documents from DrugBank database in English;
3) the PhaeDRA corpus containing MEDLINE abstracts in English.
Relation extraction accuracy for Russian and English was estimated with comparison of two multilingual Language models: XLM-RoBERTa-large and XLM-RoBERTa-sag-large. Additionaly we used the State-of-the-Art specialized models aimed at English language: bioBERT, bioALBERT, bioLinkBERT. Earlier research proved XLM-RoBERTa-sag-large to be the most efficient language model for the previous version of the RDRS dataset. We used the same approach to relation extraction included two steps: named entity recognition and relation extraction on predicted entities. Each step was estimated separately.
As a result, it is shown, that multilingual XLM-RoBERTa-sag model achieves relation extraction macro-averaged f1-score equal to 85.42% on the ground-truth named entities, 53.83% on the predicted named entities on new version of RDRS corpus. Additionally, XLM-RoBERTa-sag was estimated on the datasets for relation extraction in English (DDI2013, PhaeDRA) and achieves accuracy comparable with the top specialized models.
Consequently, XLM-RoBERTa-sag model sets the state-of-the-art for considered type of texts in Russian, and achieves accuracy comparable with the SotA results in Engilsh.
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