Polysemy as a Complexity Predictor in school textbooks

This article presents an algorithm for assessing text readability based on the analysis of polysemous words. The algorithm was tested on 30 Russian language textbooks for different grade levels, with the total size of the corpus of 1,097,170 words. We estimated values of two complexity predictors, i.e. the number of polysemous words (p1) and number of unique word senses (p2). The research demonstrated a high positive correlation between the respective parameters and text grade levels. The proposed algorithm has a potential to be applied in numerous fields that require text readability assessment and include education, law, medicine and business. The research prospects include validating the algorithm in other languages and text types, as well as contrast it with other text readability assessment algorithms.