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 in:Journal of mathematical sciences (New York, N.Y.) N.Y.), 2023-07, Vol.273 (4), p.583-594
Main Authors: Galinsky, R. B., Alekseev, A. M., Nikolenko, S. I.
Format: Article
Language:English
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