AGRR-2019: A Corpus for Gapping Resolution in Russian

Autor:	Ponomareva, Maria, Droganova, Kira, Smurov, Ivan, Shavrina, Tatiana
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to BSNLP at ACL 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.04099 Zobrazit plný text záznamu View this record from Arxiv