This study addresses a paradox in word order typology. On the one hand, the SOV order has longer dependency distances and therefore higher processing costs compared to verb-medial order. On the other hand, it is the most frequent word order in languages of the world. How come? A study of corpus data annotated with Universal Dependencies provides a simple an-swer: the costly long distances occur more rarely than one would assume because SOV clauses are infrequent in language use. A quanitative analysis of 150 Universal Dependencies corpora shows that the proportions of verb-final clauses with two overt core arguments are low across languages, including predominantly verb-final languages. Moreover, a series of Bayesian phy-logenetic models based on comparable corpora in thirty-two languages show a negative corre-lation between the proportion of verb-final clauses in a language and the average number of arguments in a clause, while controlling for argument indexing and high- and low-context cul-ture. A closer examination of argument configurations reveals a positive correlation between proportions of verb-final clauses and proportions of subjectless clauses; as for proportions of objectless clauses, the evidence is less clear. The study highlights the importance of the token-based, gradient approach to typology, which gives us insights into what kind of structures language users prefer, and what they avoid.

Nabokov, known primarily for his prose, was also a remarkable poet. The article aims to examine the semantic (lexical) features of Nabokov's images in his lyrics. These characteristics include 23 semantic (thematic) classes of words that fill two positions of the figurative model – the position of the Target of the metaphoric transfer and the position of its Source. The material includes 4 lyrical collections by Nabokov, published at different stages of his creative path – an early collection (published when he lived in Russia), 2 later collections from the Berlin period (middle stage) and one collection of mature creative activity when Nabokov lived in the USA. The article examines the relationships between the features of the two positions of the image, the distribution of semantic classes of words in the image system, and changes in the frequencies of these classes over time.

We classify texts using relative word frequencies. The task is to distinguish human-written texts from those generated by a computer using modern algorithms. We study two essay datasets, each containing an equal number of human-written and computer-generated essays. Studying Zipf diagrams shows that the generated texts have a significantly smaller vocabulary compared to human ones. However, the relative frequency of rare words (not included in the 1000 most common) does not allow us to confidently classify the texts. As additional features, we used the relative frequencies of the four most frequent words, as well as the ratio of the number of hapax legomena to the total number of different words. This feature allows to significantly improve the classification. Using these six features allows us to fairly confidently determine whether the text is computer-generated.

The present paper focuses on the statistical modelling of loanword frequencies in different semantic fields of ten selected languages of the world. The main question addressed in this paper is whether the occurrence of loanwords in different semantic fields (data are from the WOLD project) can be modelled by a (simple) power model or not. Within the framework of synergetic linguistics, the basic idea is that the transfer and integration of loanwords is a statistically well-regulated process. In the absence of deductively found models (theoretically, also a birth-and-death process could be a suitable starting point), the proposed modelling aims to shed some new light on the underlying statistical tendencies in linguistic borrowing.

Validity of the Menzerath-Altmann law has been confirmed in a number of works for lan-guages with different morphology. This research is aimed at disclosure of potential correla-tions between the structure of the Tatar word form and the average length of its constituent syllables; taking into consideration that the Turkic language family is rarely represented in quantitative linguistics, we tested the law using data from ten Tatar texts, both poetry and prose, to interpret the results from the point of view of grammar. To assess the goodness of fit of the model we applied the coefficient of determination R2 which for different texts ranged from 0.676 to 0.999. The study revealed that the examined data in Tatar generally abide by G. Altmann’s formula; the average syllable length depended both on its position in the word and on word length, and joining more affixes provided decrease in the average syl-lable length. For individual texts, while the law as a trend was observed, we discovered cer-tain fluctuations when the average syllable length for sufficiently long words was greater than for relatively short ones. Analyzing the whole corpus data we distinguished between tokens (with frequencies taken into account) and types (unique word forms considered once); the research proved the Menzerath-Altmann law valid in both cases, with better result for types.

Chinese online literature has rapidly gained popularity, displaying characteristics that differ from traditional literature, and has now become an important and unique literary genre. Chinese readers of online literature account for nearly half of the total number of Chinese internet users, approximately 537 million. This study aims to explore the linguistic characteristics of online literature through a combination of qualitative and quantitative methods. The study builds a large-scale corpus from the largest Chinese online literature website Qidian, focusing on six quantitative linguistic indicators: word frequency, lexical richness, lexical density, word vectors, punctuation distribution, and average paragraph length. Additionally, three text mining indicators—thematic concentration, information entropy, and authorial perspective—are introduced. The findings reveal that online literature features a casual and informal language style, characterized by a high frequency of function words, shorter average paragraph lengths, and significant reliance on dialogue. Furthermore, the thematic concentration is generally low, which is related to the verbosity of online works and their emphasis on narrative flow rather than detailed descriptions. Overall, this paper provides empirical data to enhance the understanding of the linguistic features of online literature and suggesting that future research could adopt broader metrics and more comprehensive text mining techniques.

We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence, which enables in-depth analysis of dependency trees with data analysis tools. We apply the polynomial-based methods to analyze sentences in the ParallelUniversal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora.