A BERT-Based Classification Model: The Case of Russian Fairy Tales

Introduction: Automatic profiling and genre classification are crucial for text suitability assessment and as such have been in high demand in education, information retrieval, sentiment analysis, and machine translation for over a decade. Of all kinds of genres, fairy tales make one of the most challenging and valuable objects of study due to its heterogeneity and a wide range of implicit idiosyncrasies. Traditional classification methods including stylometric and parametric algorithms, however, are not only labour-intensive and time-consuming, but they are also struggling with identifying corresponding classifying discriminants. The research in the area is scarce, their findings are still controversial and debatable. Purpose: Our study aims to fill this crucial void and offers an algorithm to range Russian fairy-tales into classes based on the pre-set parameters. We present the latest BERT-based classification model for Russian fairy tales, test the hypothesis of BERT potential for classifying Russian texts and verify it on a representative corpus of 743 Russian fairy tales. Method: We pre-train BERT using a collection of three classes of documents and fine-tune it for implementation of a specific application task. Focused on the mechanism of tokenization and embeddings design as the key components in BERT’s text processing, the research also evaluates the standard benchmarks used to train classification models and analyze complex cases, possible errors and improvement algorithms thus raising the classification models accuracy. Evaluation of the models performance is conducted based on the loss function, prediction accuracy, precision and recall. Results: We validated BERT’s potential for Russian text classification and ability to enhance the performance and quality of the existing NLP models. Our experiments with cointegrated/rubert-tiny, ai forever/ruBert-base, and DeepPavlov/rubert-base-cased-sentence on different classification tasks demonstrate that our models achieve state-of-the-art results with the best accuracy of 95.9% in cointegrated/rubert-tiny thus outperforming the other two models by a good margin. Thus, the achieved by AI classification accuracy is so high that it can compete with that of human expertise. Conclusion: The findings highlight the importance of fine-tuning for classifying models. BERT demonstrates great potential for improving NLP technologies and contributing to the quality of automatic text analysis and offering new opportunities for research and application in a wide range of areas including identification and arrangement of all types of content-relevant texts thus contributing to decision making. The designed and validated algorithm can be scaled for classification of as complex and ambiguous discourse as fiction thus improving our understanding of text specific categories. Considerably bigger datasets are required for these purposes.