PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track2. Modern Machine Learning Methods
Relation Extraction from Texts Containing Pharmacologically Significant Information on base of Multilingual Language Models
A.A. Selivanov*, A. Gryaznov, R. Rybka, A. Sboev, S. Sboeva and Y. Klueva
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Pre-published on: November 14, 2022
Published on: December 06, 2022
In this paper we estimate the accuracy of the relation extraction from texts containing pharmacologically significant information on base of the expanded version of RDRS corpus, that contains texts of internet reviews on medications in Russian.

The accuracy of relation extraction was estimated and compared for two multilingual language models: XLM-RoBERTa-large and XLM-RoBERTa-large-sag. Earlier research proved XLM-RoBERTa-large-sag to be the most efficient language model for the previous version of the RDRS dataset for relation extraction using a ground-truth named entities annotation. In the current work we use two-step relation extraction approach: automatic named entity recognition and relation extraction on predicted entities. The implemented approach gave an opportunity to estimate the accuracy of the proposed solution to the relation extraction problem, as well as to estimate the accuracy at each step of the analysis.

As a result, it is shown, that multilingual XLM-RoBERTa-large-sag model achieves relation extraction macro-averaged f1-score equals to 86.4% on the ground-truth named entities, 60.1% on the predicted named entities on the new version of the RDRS corpus contained more than 3800 annotated texts. Consequently, implemented approach based on the XLM-RoBERTa-large-sag language model sets the state-of-the-art for considered type of texts in Russian.
DOI: https://doi.org/10.22323/1.429.0014
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