Classification of Russian Texts by Genres Based on Modern Embeddings and Rhythm

Abstract

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 genres: novels, scientific articles, reviews, posts from the social network Vkontakte, news from OpenCorpora. Visualization and analysis of statistics for rhythm features made it possible to identify both the most diverse genres in terms of rhythm: novels and reviews, and the least ones: scientific articles. Subsequently, these genres were classified best with the help of rhythm features and the neural network-classifier LSTM. Clustering and classifying texts by genre using ELMo and BERT embeddings made it possible to separate one genre from another with a small number of errors. The multiclassification F-score reached 99%. The study confirms the efficiency of modern embeddings in the tasks of computational linguistics, and also allows to highlight the advantages and limitations of the complex of rhythm features on the material of genre classification.

Genre Classification of Books in Russian with Stylometric Features: A Case Study

“…Not much analogous research has been conducted on datasets in the Russian language. The authors of [2] investigated contemporary vector text models, such as ELMo embeddings, the BERT language model, and a complex of numerical rhythm features, for genre categorization of texts written in the Russian language. Their experiments used ten thousand texts from five different genres: OpenCorpora news, Vkontakte communications, reviews, scientific publications, and novels.…”

“…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.…”

“…We perform an extensive evaluation of binary and multi-class genre classification on a subset of our dataset and analyze the results; we employ a wide range of text representations, including stylometric features [2]. The purpose of this evaluation is to show that genre classification of Russian books is a highly nontrivial task.…”