Improving Neural Models for Natural Language Processing in Russian with Synonyms

Large-scale neural network models, including models for natural language processing, require large datasets that could be unavailable for low-resource languages or for special domains. We consider a way to approach the problem of poor variability and small size of available data for training NLP mod...

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Bibliographic Details
Published inJournal of mathematical sciences (New York, N.Y.) Vol. 273; no. 4; pp. 583 - 594
Main Authors Galinsky, R. B., Alekseev, A. M., Nikolenko, S. I.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.07.2023
Springer
Springer Nature B.V
Subjects
Computational linguistics
Data mining
Language processing
Mathematics
Mathematics and Statistics
Natural language interfaces
Natural language processing
Neural networks
Sentiment analysis
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Summary:Large-scale neural network models, including models for natural language processing, require large datasets that could be unavailable for low-resource languages or for special domains. We consider a way to approach the problem of poor variability and small size of available data for training NLP models based on augmenting the data with synonyms. We design a novel augmentation scheme that includes replacing words with synonyms, apply it to the Russian language and report improved results for the sentiment analysis task.
ISSN:1072-3374
1573-8795
DOI:10.1007/s10958-023-06520-z