PoS - Proceedings of Science
Volume 410 - The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021) - Regular papers
Evaluation of Machine Learning Methods for Relation Extraction Between Drug Adverse Effects and Medications in Russian Texts of Internet User Reviews
A. Sboev*, A. Selivanov, R. Rybka, I. Moloshnikov and G. Rylkov
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Pre-published on: December 01, 2021
Published on: January 12, 2022
The research considers an automatic extraction of relations between mentions of medications and adverse drug reactions in Russian-language drug reviews. This text analyzing method might be useful for pharmacovigilance and medicines reprofiling. Its application to Russian-language reviews hasn't been studied yet due to the lack of corpora with relation annotation in Russian.
The study is aimed at solving this problem. It is based on the original dataset gathered by our group. It consists of annotated relations between entities from the Russian Drug Review Corpus, that contains the Internet users' reviews on medications in Russian language. Computational experiments were carried out on developed corpora using classical machine learning methods, as well as a more advanced neural network model based on Transformer layers -- XLM-RoBERTa-sag. The list of applied classical machine learning methods consists of support vector machine, logistic regression, Naive Bayes classifier and gradient boosting. The concatenation of TF-IDF entity vectors of character n-grams was used as a text representation. Based on a set of experiments, the following hyperparameters of these methods were selected: the size of n-grams and the limitation on the frequency of occurrence of n-grams (too rare or too frequent n-grams were excluded from the feature vector). For XLM-RoBERTa-sag, the input data is represented as usual for such type of models (language models based on Transformer topology). The following input text representation types were considered during the experiments: a whole text, a text of target entity pairs; a text of target entity pairs with words between them; a text of target entity pairs and the whole input text, the latter input type is the one that maximizes accuracy. It is shown that XLM-RoBERTa-sag model achieves a result of 95%, according to the macro-averaged f1 metric, which is the state-of-the-art result of recognition of the relations between mentions of adverse drug reactions and medications in Russian-language online reviews. The Naive Bayes classifier with multivariate normal distribution achieves the best result among classical machine learning methods: 75%, which exceeds the result of random label generation by 21%.
DOI: https://doi.org/10.22323/1.410.0006
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