Generating Learner-Like Morphological Errors in Russian

To speed up the process of categorizing learner errors and obtaining data for languages which lack error-annotated data, we describe a linguistically-informed method for generating learner-like morphological errors, focusing on Russian. We outline a procedure to select likely errors, relying on guiding stem and suffix combinations from a segmented lexicon to match particular error categories and relying on grammatical information from the original context.