AGRR-2019: A Corpus for Gapping Resolution in Russian
It addresses the need for resources to process gapping in Russian, which is incremental as it builds on existing datasets for other languages.
The paper introduces AGRR-2019, a corpus of 7.5k sentences with gapping and 15k negative sentences from multiple genres, created for a shared task to develop NLP tools for ellipsis processing in Russian.
This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.