## 7 Discussion and Conclusions  
During the course of the research, the authors analyzed modern approaches to determining the author of a natural-language text, implemented approaches of authorship attribution based on SVM and deep NNs architectures, evaluated the developed approaches on different numbers of authors and volumes of texts, and evaluated the resistance of the approaches to anonymization techniques. The results obtained allow us to draw several conclusions. Firstly, despite the great popularity of deep NNs architectures, they are inferior to the traditional SVM machine learning algorithms in accuracy by more than 10% on average. This is due to the fact that NNs require more data for learning than SVM to extract informative features from the text. However, when solving real-life authorship identification tasks, the number of data could be not enough for accurate decision-making by the NN. Secondly, the SVM classification is based on an accurately found set of features manually formed by experts. Such informative features are also obvious for anonymization techniques and therefore can be removed or significantly corrupted. Thus, to solve the problem of identification of the author of a natural language text, both the SVM-based approach and deep models proposed by authors are equally suitable. However, when choosing an approach, the researched data and available technical resources should be objectively evaluated. In the case of a lack of resources, an SVM approach should be used. If there are traces of use anonymization in the text, despite the longer processing time, deep NNs architectures are recommended because they can find both the obvious and unobvious dependences in the text. Thirdly, when using SVM, we recommended using five of the most informative features of the author's style that may improve the authorship identification process: unigrams and trigrams of Russian letters, high-frequency words, punctuation marks, and distribution of words among parts of speech. Finally, based on the results obtained, as well as on the experience of earlier research, the authors identified the important criteria to obtain accurate results when identifying the author of a natural language text:  
1. Author's personality: the informative features extracted from the text should contain all important information about the writing style. In this case, the authorship attribution system will be able to distinguish between the same or different authors. 2. Invariance: the writing style's characteristics should be stable for certain reasons, e.g., author's mood, emotional state, and the subject of the text. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Authors** & **Model** & **Accuracy before Anonymization** & **Accuracy after Anonymization** \\ \hline \multirow{4}{*}{10} & SVM & 0.97 & 0.46 \\  & SVM (with aspects) & 0.98 & 0.52 \\  & LSTM & 0.84 & 0.66 \\  & CNN with attention & 0.93 & 0.78 \\  & Transformer & 0.94 & 0.81 \\ \hline \multirow{4}{*}{30} & SVM & 0.95 & 0.42 \\  & SVM (with aspects) & 0.97 & 0.49 \\  & LSTM & 0.72 & 0.63 \\  & CNN with attention & 0.91 & 0.71 \\  & Transformer & 0.93 & 0.74 \\ \hline \multirow{4}{*}{50} & SVM & 0.93 & 0.39 \\  & SVM (with aspects) & 0.95 & 0.44 \\ \cline{1-1}  & LSTM & 0.68 & 0.59 \\ \cline{1-1}  & CNN with attention & 0.75 & 0.68 \\ \cline{1-1}  & Transformer & 0.77 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average accuracy of author identification using Transformer. 3. Stability: the writing style may be influenced by the imitation of another author's writing style or by deliberately distorting the author's own style for other reasons. The chosen approach should be resistant to such actions. 4. Adaptability to the style of text: the author adapts to the specificities of the selected style of text to follow it. Adaptability to the style of text leads to significant changes in the characteristics of the author's writing style. In addition, when writing official documents, many people use ready templates and just fill in their own data.