## 2 Related Works
Such algorithms should overcome the problem of a lack of information about all candidate authors during training. Functional words have been chosen as a feature, as they are quite likely to appear even in small samples and can therefore be particularly effective for analyzing social networks. Combinations of different sets of \(n\)-grams at symbol and word level with \(n\)-grams at the part-of-speech level were investigated. An accuracy of 70% was obtained for 50 authors. The main idea of the study [13] is to modify the approach to establishing authorship by combining it with pre-trained language models. The corpus of texts consisted of essays by 21 undergraduate students written in five formats (essay, email, blog post, interview, and correspondence). The method is based on a recurrent neural network (RNN) operating at the symbol level and a multiheaded classifier. In cross-thematic authorship determination, the results were 67-91%, depending on the subject, and in cross-genre, 77-89%, depending on the genre. The essence of [14] is to research document vectors based on \(n\)-grams. Experiments were conducted on a cross-thematic corpus containing some articles from 1999 to 2009 published in the English newspaper _The Guardian_. Articles by 13 authors were collected and grouped into five topics. To avoid overlapping, those articles for which content included more than one category were discarded. The results show that the method is superior to linear models based on \(n\)-gram symbols. To train the Doc2 vec model, the authors used a third-party library called GENSIM 3. The best results were achieved on texts of large sizes. Accuracy for different categories ranged from 90.48 to 96.77%. In [15], an ensemble approach that combines predictions made by three independent classifiers is presented. The method based on variable-length \(n\)-gram models and polynomial logistic regression and used to select the highest likelihood prediction among the three models. Two evaluation experiments were conducted: using the PAN-CLEF 2018 test dataset (93% accuracy) and a new corpus of lyrics in English and Portuguese (52% accuracy). The results demonstrate that the proposed approach is effective for fiction texts but not for lyrics. The research conducted in [16] used the support vector machine (SVM). Parameters for defining the writing style were highlighted at different levels of the text. The authors demonstrated that more complex parameters are capable of extracting the stylometric elements presented in the texts. However, they are most efficiently used in combination with simpler and more understandable \(n\)-grams. In this case, they improve the result. The dataset included 20 samples in four different languages (English, French, Italian, and Spanish). Thus, five samples from 500 to 1000 words in each language were used. The challenge was to assign each document in the set of unknown documents to a candidate author from the problem set. The results were 77.7% for Italian, 73% for Spanish, 68.4% for French, and 55.6% for English. Authorship identification methods are used not only for literary texts but also to determine plagiarism in scientific works. For example, [17] presents a system for resolving the ambiguity of authorship of articles in English using Russian-language data sources. Such a solution can improve the search results for articles by a specific author and the calculation of the citation index. The link.springer.com database was used as the initial repository of publications, and the eLIBRARY.ru scientific electronic library was used to obtain reliable information about authors and their articles. To assess the quality of the comparison, experiments were carried out on the data of employees of the A.P. Yershov Institute of Informatic Systems. The sample included 25 employees, whose publications are contained in the link.springer.com system. To calculate the similarity rate of natural language texts, they were presented as vectors in multidimensional space.