Readability formulas for three levels of Russian school textbooks

In this work, we propose a new text complexity formula aimed at assessing the complexity of Russian school textbooks. We used the annotated Russian Academic Corpus containing over 5 million tokens as the training and validation data and employed machine learning methods in the study. The values of 4 parameters in each of the 154 texts used for the research were measured with the help of the tools from the Spacy library. Comparative analysis of the new and existing complexity formulas suggests that the differences between them are indicative and the new formulas provide more accurate results. This research advances our understanding of the interdependency between frequency and text complexity and provides a framework for effective implementation of lexical frequency patterns in discourse complexity studies. The findings can be implemented by textbooks writers and test developers to select and modify texts for specific categories of readers. Other areas of application include website design, surveys, and semantic analysis of social networks.