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The article investigates modern vector text models for solving the problem of genre classification of Russian-language texts. Models include ELMo embeddings, BERT language model with pre-training and a complex of numerical rhythm features based on lexico-grammatical features. The experiments were carried out on a corpus of 10,000 texts in five genr...
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... A text's stylistic features serve as markers of different genres and are frequently employed for automatic analysis in this field because they represent a text's structural quirks, among other things [1,2]. Automatic genre classification makes it possible to solve several computational linguistics problems more quickly, including figuring out a word or phrase's meaning or part of speech, locating documents that are pertinent to a semantic query [3], improving authorship attribution [4][5][6], and more [2,7]. ...
... A text's stylistic features serve as markers of different genres and are frequently employed for automatic analysis in this field because they represent a text's structural quirks, among other things [1,2]. Automatic genre classification makes it possible to solve several computational linguistics problems more quickly, including figuring out a word or phrase's meaning or part of speech, locating documents that are pertinent to a semantic query [3], improving authorship attribution [4][5][6], and more [2,7]. ...
... These texts span 5 billion words; however, out of the six genres, only two can be attributed to literature-fiction and poetry. The corpus of [2] contains 10,000 texts assigned to five different genres-novels, scientific articles, reviews, posts from the VKontakte social network [10], and news texts from OpenCorpora [11], the open corpus of Russian texts. Only one genre in this corpus is a literature genre-the novels. ...
Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy.
