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This paper provides a comprehensive overview of the gapping dataset forRussian that consists of 7.5k sentences with gapping (as well as 15k relevantnegative sentences) and comprises data from various genres: news, fiction,social media and technical texts. The dataset was prepared for the AutomaticGapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed atstimulating the development of NLP tools and methods for processing ofellipsis.  In this paper, we pay special attention to the gapping resolution methodsthat were introduced within the shared task as well as an alternative test setthat illustrates that our corpus is a diverse and representative subset ofRussian language gapping sufficient for effective utilization of machinelearning techniques.

AGRR-2019: A Corpus for Gapping Resolution in Russian