Vladimir Vladimirovich Bochkarev

Monkey texts are among theoretical models used to explain Zipf’s law. Such models assume that each subsequent symbol (including space marking the end of a current word) is chosen at random. Probabilities of choosing a particular symbol are assumed to be either independent of other symbols or dependent only on the previous one. In the latter case, a text is obviously a Markov chain. In recent works, possible distributions of word frequencies in generated texts have been classified in the Markov chain approximation depending on the structure of transition probability matrix. Along with the cases of strong and weak power laws, there are also theoretically possible cases when the frequency distribution obeys exponential or subexponential laws. However, cases of such distribution in natural languages have not yet been described. The issue of large deviations from Zipf’s law in the Chinese language has been discussed in some works. In this paper, we analyze frequency distribution of Chinese words using data from the Google Books Ngram corpus and show that it can be approximated with high accuracy by a subexponential dependence. Thus, the first example of a subexponential distribution of word frequencies in natural language has been found.

Context and relevance. The study of collective emotions is crucial for understanding the socio-psychological state of society during periods of radical transformation. The 1990s in Russia, marked by cultural trauma, are of particular interest for analyzing the dynamics of public sentiment through the lens of media discourse and subjective assessments. Objective: to assess the dynamics of affectivity in newspaper texts during the 1990s, identify its connection with key socio-political events, and conduct a comprehensive comparison with data on the emotional state of society from sociological and socio-psychological surveys. Hypothesis. Media materials reflect the general direction of the society's psycho-emotional state; however, a shift manifesting as the “Pollyanna effect” (a bias towards positive evaluations in retrospect) would be observed in respondents' subjective assessments. Methods and materials. A psycholingustic analysis corpus of 56,789 texts from the “Arguments and Facts” newspaper (1983—2007) was conducted using machine-readable dictionaries to assess R. Plutchik's emotions and C. Osgood's factors. The data were compared with results from a Levada Center[1] sociological survey (contemporary assessments from the 1990s) and a 2024 socio-psychological survey (retrospective assessments, N = 1044). Results. A clear dynamics of affectivity in newspaper texts, correlating with key events of the era, was identified. Discrepancies between objective linguistic markers and subjective assessments were found: retrospective assessments revealed the formation of an “emotional concentrate” with more intense negative experiences compared to press data and contemporary surveys from the 1990s. Conclusions. Newspaper discourse is a relevant but mediated indicator of collective emotions. The identified discrepancies confirm the operation of psychological mechanisms of retrospective distortion and the “Pollyanna effect” in collective memory, highlighting the need for an integrated approach to studying the emotional state of society. [1] ANO Levada Center has been included by the Ministry of Justice in the register of non-profit organizations performing the functions of a foreign agent.

The relevance of the study is determined by the fact that the variability of aspectual affixes in the Russian language has not been fully studied. Meanwhile, super-large diachronic corpora of texts provide the opportunity of a detailed quantitative description of verbal affixes evolution. The aim of the study is to identify objective syntagmatic and paradigmatic factors (extrasyllabic synharmonicity, word morphological structure evolution, lexical compatibility, style) that determine the choice of one of the competing suffixes among variable verbs with the redundant paradigm with secondary imperfection -a-(-ya-)/-yva-(-iva-) . The authors used the Russian language corpus of the Google Books Ngram corpus, including data on the word forms frequency over the past 200 years. The authors used methods of corpus analysis and diachronic quantitative analysis. The study revealed psychophonetic, morphological, semantic, and stylistic factors affecting the linguistic changes of redundant verbal paradigms. The authors came to the conclusion that, contrary to popular belief, the former na -a-(-ya-) variant is not replaced with the -iva-(-yva-) form in most cases; according to frequency dynamics, a temporary increase in the popularity of forms of secondary imperfection in general was followed by a return to the primary forms of Imperfect in recent decades. In rare cases, where a form with a suffix denoting iteration is approved, the form was chosen because of the root vowel, “l-epenteticum”, and verb prefixes. The frequency and prevalence of the verb hypothetically preventing the emergence of variants or unification had no effect on language changes. The developed approach can be applied to study evolution of competing word forms in a wide range of cases.

Introduction: In recent works, a new psycholinguistic concept has been introduced and studied that is socialness of a word. A socialness rating reflects word social significance and dictionaries with socialness ratings have been compiled using either a survey or machine method. Unfortunately, the size of the dictionaries with word socialness ratings created by a survey method is relatively small. Purpose: The study objective is to compile a large dictionary with English word socialness ratings by using machine extrapolation, transfer the rating estimations to other languages as well as to obtain diachronic models of socialness ratings. Method: The socialness ratings of words are estimated using multilayer direct propagation neural networks. To obtain synchronic estimates, pre-trained fasttext vectors were fed to the input. To obtain diachronic estimates, word co-occurrence statistics in a large diachronic corpus was used. Results: The obtained Spearman`s correlation coefficient between human socialness ratings and machine ones is 0.869. The trained models allowed obtaining socialness ratings for 2 million English words, as well as a wide range of words in 43 other languages. An unexpected result is that the linear model provides highly accurate estimate of the socialness ratings, which can be hardly further improved. Apparently, this is due to the fact that in the space of vectors representing words there is a selected direction responsible for meanings associated with socialness driven by of social factors influencing word representation and use. The article also presents a diachronic neural network predictor of concreteness ratings using word co- occurrence vectors as input data. It is shown that using a one-year data from a large diachronic corpus Google Books Ngram one can obtain accuracy comparable to the accuracy of synchronic estimates. Conclusion: The created large machine dictionary of socialness ratings can be used in psycholinguistic and cultural studies. Changes in socialness ratings can serve as a marker of word meaning change and be used in lexical semantic change detection

The article is devoted to the competition of borrowed and native vocabulary in the Russian language in the 18th–20th centuries. The relevance of the study: due to the large number of non-native speakers, the etymological picture of Russian vocabulary is somewhat shifted towards the dominance of internationalisms, characterized by their universality and conventionality in most languages. This fact predetermines a spontaneous transformation of the typical lexical tools of scientific, official business and journalistic speech, and calls for the need to change the lexicographic norms.Our analysis has shown that internationalisms, denoting already existing concepts, can displace native words, especially in literary speech. The article identifies linguistic patterns in lexical combinability of native and borrowed words, and determines stylistic differences between them. When working with text corpora, we have revealed discrepancies in syntactic combinability between synonyms, which allows us to state the stylistic demarcation of borrowings and native Russian words. Etymological synharmonism and connotation show the way words of different genesis form context. We studied the change in the frequency of native and borrowed words’ usage in diachrony using text corpora.As a result, we have come to the following conclusions: borrowed words have a clearer semantic orientation in the Russian language; they are characterized by a lower frequency and narrow combinability. We have analyzed the cases of native lexemes’ displacement by foreign-language borrowings from the point of view of their frequency growth dynamics in the corpus. We have determined the average duration of the period, during which a foreign word exceeds its analog frequency of the Slavic origin in book speech – 40-60 years. The results obtained are important for studying the deep changes in society, since we are talking about internationalisms that denote already existing social phenomena or objects.