## 3 Related Works
The experimental results show that the proposed model is superior to traditional Doc2 vec descriptions based on unigrams of words. However, even here the authors of the work note that the greatest accuracy (at least 90%) is achieved on large texts. A number of works attempt to explore the rhythmic component of texts. Thus, in [21], the authors propose to use phonological information about the tones and rhymes of the Chinese language, automatically extracted from unannotated texts, for the attribution of authorship. Support vector machines and algorithms based on random forests were used as classifiers. The article [22] gives the performance evaluation of the ProseRhythmDetector (PRD), the text rhythm analysis tool, for prose texts in English and Russian. The study was conducted on the basis of 50 English and 50 Russian literary texts written over the last two centuries and consisting of approximately 88,000 words each. The PRD tool was developed for the quantitative analysis of rhythm figures containing the repetition in their structure. The paper evaluates the accuracy of the PRD tool when detecting stylistic features describing the repetitions in sentences. The analysis of rhythmic figures helps to identify the idilects of the authors and to draw conclusions about the uniqueness of their style and language, which is directly related to the problem of linguistic uniqueness and identification of the authors. The authors of the work note that, in this regard, the tool has shown encouraging results. The paper also discusses the typical errors in the operation of the tool, analyzes the controversial cases, and provides the recommendations for the usage of the tool to identify the author and his idiolect. A number of authors successfully use the various graph representations to describe the texts and to extract from them the attributes of the author's style and attribution, as well as for annotation and semantics analysis. The graphs are also used to describe the universal dependencies. In [23,24], the adjacency graphs are used to analyze the text properties and to extract information about language styles. In the work [24], the prose literary texts in Polish and English are studied. In the adjacency graphs, the text is represented by a set of words and their word combinations; in this context, a language is understood as a complex system of higher levels that cannot be reduced to the sum of the elements involved, as, for example, the meanings of individual words do not necessarily provide the understanding of the entire sentence. Before building an adjacency network, the text undergoes the preliminary processing: annotation and unnecessary spaces are removed, all letters are converted to lowercase, and punctuation marks are replaced with special symbols so that they are then processed in the same way as words due to the fact that, from the point of view of statistical properties, the punctuation marks are no less valuable than the words, and they are the carriers of information about the language sample. In the adjacency networks, each vertex represents a single word, and the edges indicate whether words in the text occur next to each other. In the weighted version of the network, the weights represent the number of words encountered together. In this work, unlike in [23], the words are not lemmatized when constructing a network, which allows us to take into account the characteristic forms of words. A number of characteristics are calculated for each network: vertex degrees, shortest path lengths, clustering coefficients, as well as associativity and modularity coefficients. After that, the hierarchical clustering and the ensembles of decision trees are used for the studied properties. The accuracy of attribution of authorship using the described approach exceeded 90%, but it should be noted that the work investigated novels, that is, texts of considerable size.