Abstract
In this study, we test several augmentation and distant supervision techniques to increase sentiment datasets in Russian. We use transfer learning approach pre-trained on created additional data to improve the performance. We compare our proposed approach based on distant supervision with existing augmentation methods. The best results were achieved using three-step approach of sequential training on general, thematic and original train samples. The results were improved by more than 3% to the current state-of-the-art methods for most of the benchmarks using data automatically annotated with distant supervision technique.
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The reported study was funded by RFBR according to the research project â„–Â 20-07-01059.
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Golubev, A., Loukachevitch, N. (2021). Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian. In: EkÅ¡tein, K., Pártl, F., KonopÃk, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_16
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DOI: https://doi.org/10.1007/978-3-030-83527-9_16
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