## 2 Related Works
The study was conducted based on three datasetsin Russian (Habrahabr blog--30 authors, average text length 2000 words; vk.com--50 and 100 authors, average text length 100 words; Echo.msk.ru--50 and 100 authors, average text length 2000 words). The best results were achieved by CNN (87% for Habrahabr blog, 59% and 53% for 50 and 100 authors with vk.com, respectively). Character's trigrams performed significantly better for short texts from social networks, while for longer texts, both trigram and tetragram representations achieved almost the same accuracy (84% for trigrams, 87% for tetragram representations). The object of research study [24] is journalistic articles from Russian pre-revolutionary magazines. The information system Statistical Methods of Literary Texts Analysis (SMALT) has been developed to calculate various linguistic and statistical features (distribution of parts of speech, average word and sentence length, vocabulary diversity index). Decision trees were used to determine the authorship. The resulting accuracy was 56%. The problem of authorship attribution of short texts obtained from Twitter was considered in scientific work [25]. Authors proposed a method of learning text representations using a joint implementation of words and character n-grams as input to the NNs. Authors used an additional feature set with 10 elements: text length, number of usenames, topics, emoticons, URLs, numeric expressions, time expressions, date expressions, polarity level, and subjectivity level. Two series of comparative experiments were provided to test using CNN and LSTM. The method achieved an accuracy of 83.6% on the corpus containing 50 authors. The authors of [26] applied integrated syntactic graphs (ISGs) to the task of automatic authorship attribution. ISGs allow for combining different levels of language description into a single structure. Textual patterns were extracted based on features obtained from the shortest path walks over integrated syntactic graphs. The analysis was provided on lexical, morphological, syntactic, and semantic levels. Stanford dependency parser and WordNet taxonomy were applied in order to obtain the parse trees of the sentences. The feature vectors extracted from the ISGs can be used for building syntactic \(n\)-grams by introducing them into machine learning methods or as representative vectors of a document collection. Authors showed that these patterns, used as features, allow determining the author of a text with a precision of 68% for the C10 corpus and also performed experiments for the PAN'13 corpus, obtaining a precision of 83.3%. An approach based on joint implementation of words, \(n\)-grams, and the latent Dirichlet allocation (LDA) was presented in [27]. The LDA-based approach allows the processing of sparse data and volumetric texts, giving a more accurate representation. The described approach is an unsupervised computational methodology that is able to take into account the heterogeneity of the dataset, a variety of text styles, and also the specificity of the Urdu language. The considered approach was tested on 6000 texts written by 15 authors in Urdu. The improved sqrt-cosine similarity was used as a classifier. As a result, an accuracy of 92.89% was achieved. The idea of encoding the syntax parse tree of a sentence into a learnable distributed representation is proposed in [28]. An embedding vector is created for each word in the sentence, encoding the corresponding path in the syntax tree for the word. The one-to-one correspondence between syntax-embedding vectors and words (hence their embedding vectors) in a sentence makes it easy to integrate obtained representation into the word-level Natural Language Processing (NLP) model. The demonstrated approach has been tested using CNN. The model consists of five types of layers: syntax-level feature embedding, content-level feature embedding, convolution, max pooling, and softmax.