## 2 Related Works
To construct a vector representation of texts, a bag-of-words algorithm was used with the term frequency-inverse document frequency (TF-IDF) measure. Stop-words were preliminarily removed from the texts, and stemming of words was carried out. Experiments were also provided on the vectorization of natural language texts using the word2 vec. The average percentage of the number of publications of authors recognized by the system was 79%, while the number of publications that did not belong to the author but were assigned to his group was close to zero. The approaches used in the system are applicable for disambiguating authorship of publications from various bibliographic databases. The implemented system showed a result of 92%. There were only a few works that achieved a high level of author identification in Arabic texts. In [18], the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) was used to select the basic classifier of the ensemble. More than 300 stylometric parameters were extracted as attribution features. The AdaBoost and Bagging methods were applied to the dataset in Arabic. Texts were taken from six sources. Corpora included both short and long texts by three hundred authors writing in various genres and styles. The final accuracy was 83%. A new area of research is attribution, which uses not only human-written texts but also texts obtained using generation [19]. Several recently proposed language models have demonstrated an amazing ability to generate texts that are difficult to distinguish from those written by humans. In [20], a study of the problem of authorship attribution is proposed in two versions: determining the authorship of two alternative human-machine texts and determining the method that generated the text. One human-written text and eight machine-generated texts (CTRL, GPT, GPT2, GROVER, XLM, XL-NET, PPLM, FAIR) were used. Most generators still produce texts that significantly differ from texts written by humans, which makes it easier to solve the problem. However, the texts generated by GPT2, GROVER, and FAIR are of significantly better quality than the rest, which often confuses classifiers. For these tasks, convolutional neural networks (CNN) were used, since the CNN architecture is better suited to reflect the characteristics of each author. In addition, the authors improved the implementation of the CNN using \(n\)-gram words and part-of-speech (PoS) tags. The result in the \"human-machine\" category ranges from 81% to 97%, depending on the generator, and, for determining the generation method, 98%. The author of [21] presented the software product StylometRy, which allows the identification of the author of a disputed text. Texts were presented in the form of a bag-of-words model. Naive Bayesian classifier, \(k\)-nearest method, and logistic regression were chosen as classifiers, and pronouns were used as linguistic features. The models were checked in [PERSON], [PERSON], and [PERSON] texts. The minimum text volume for analysis was 5500 words. The accuracy of the model for texts over 150,000 characters was in the range of 60-100% (average 87%). The scientific work [22] describes the features of four styles of the Russian language --scientific, official, literary, and journalistic. The parameters selected for texts analysis were: the ratio of the number of verbs, nouns, adjectives, pronouns, particles, and interjections to the number of words in the text, the number of \"noun + noun\" constructions, the number of \"verb + noun\" constructions, the average word length, and the average sentence length. Decision trees were used for classification. The accuracy of the analysis of 65 texts of each style was 88%. The highest accuracy was achieved when classifying official and literary texts, and the lowest was achieved for journalistic texts. The authors of [23] present the analysis and application of various NNs architectures (RNN, LSTM, CNN, bi-directional LSTM).