## 2 Related Works  
Research on concreteness/abstractness is conducted broadly from psychology and psycholinguistics to neurophysiology and medicine. A fresh overview can be found in [14]. In neurophysiology, the localization of the concepts of concreteness/abstractness was studied extensively. In many experiments using neuroimaging techniques, it has been shown that concrete and abstract words are represented in different neuroanatomical structures of the brain. In psychological research, the so-called 'effect of concreteness' has been established, demonstrating the greater ease of processing specific words in the human mind. Concrete words are better remembered [15], better recognized [16], read faster [17], and learned faster [18]. Dictionary definitions are easier to write and more detailed [19]. Concrete words are easier to associate [20]. The concepts of concreteness and abstractness have been subjects of study in linguistics for a long time. However, recently, with the emergence of large corpora of texts and large lexical ontologies, fundamentally new research ideas and results have appeared. The most interesting is the following. In [21], it is shown that, over time,the degree of specificity of words increases. In [22], it is shown that the density of the set of semantically close words is higher for concrete words compared to abstract ones. In [23], it is noticed that in text corpora, abstract words are more often found together with abstract words and concrete ones with concrete ones. The work [24] compares the categories of concreteness and specificity. To conduct psychological and neurophysiological experiments, lists of words with estimates of the degree of their concreteness/abstractness are needed. Such lists are created by interviewing native speakers and extrapolating human estimates by machine methods. A significant number of works have been devoted to the development of machine extrapolation methods: [25; 26; 27; 28; 29; 30; 31; 32; 33]. Early work was based on thesauri such as WordNet, with ratings shifted to synonyms. The next step was using vector semantics applicable to large corpora of texts. Rankings for new words were based on the semantic proximity of words in the constructed semantic space [25; 28; 30; 31]. Thus, to create a computer dictionary in any language, a necessary condition is the existence of a large body of texts, based on which vector semantics can be built. Vector semantics was constructed using various methods: Latent Semantic Analysis, the High Dimensional Explorer model [34], the Skipgram vector-embedding model [35], etc. Evaluation of the quality of machine dictionaries is also of fundamental importance. They are assessed by comparison with human ones with the calculation of the correlation coefficient of the two dictionaries, most often according to Spearman. By far, the best result achieved is the machine dictionary for English of work [36], which has a human correlation coefficient of 0.9. The dictionary is built using fastText technology to build a semantic space and SVM as a classifier. Extrapolation of human estimates is done by cross-validation against a 40,000-word dictionary [5]. In [5], two human dictionaries were compared, and it was found that the correlation coefficient between them was 0.919. It is natural to interpret this value as the maximum possible for machine extrapolation. In most papers, only one fixed-size dictionary is considered. In [12], three English dictionaries of different sizes are taken, which makes it possible to reveal the dependence of the quality of extrapolation on the size of the dictionary. For dictionaries of size 22,797, 4061, and 3000 words, the correlation scores were 0.887, 0.872, and 0.848, respectively, which clearly indicates a dependence on the size of the dictionary. The larger the dictionary, the more accurate the extrapolation.