Exploring the Feature Space for Cross-Domain Assessing the Complexity of Russian-Language Texts

The assessment of text complexity is a significant applied problem with potential applications in drafting legal doc-uments, editing textbooks, and selecting books for extracurricular reading. Different task formulations give rise to various types of text complexity that are weakly correlated. Despite this, re-searchers typically overlook cross-domain complexity assessment. This study evaluates the applicability of various linguistic features in assessing the complexity of Russian-language texts, adding two new groups of features (rhythmic and cohesion) to those previously studied and introducing a new group of features for lexical complexity. We perform both in-domain and cross-domain comparisons of the features. Our findings indicate that syntactic features are the most significant in terms of Mutual Information. In the in-domain context, lexical and morphological features were found to be the most beneficial, whereas in the cross-domain context, syntactic, morphological, and lexical features proved to be the most effective. Conversely, rhythmic and cohesion features did not significantly impact the quality of the assessment algorithms.