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I investigate Russian second language readability assessment using a machine-learning approach with a range of lexical, morphological, syntactic, and discourse features. Testing the model with a new collection of Russian L2 readability corpora achieves an F-score of 0.671 and adjacent accuracy 0.919 on a 6-level classification task. Information gain and feature subset evaluation shows that morphological features are collectively the most informative. Learning curves for binary classifiers reveal that fewer training data are needed to distinguish between beginning reading levels than are needed to distinguish between intermediate reading levels.

Insights from Russian second language readability classification: complexity-dependent training requirements, and feature evaluation of multiple categories