@inproceedings{ponomareva-etal-2019-agrr,
title = "{AGRR} 2019: Corpus for Gapping Resolution in {R}ussian",
author = "Ponomareva, Maria and
Droganova, Kira and
Smurov, Ivan and
Shavrina, Tatiana",
editor = "Erjavec, Toma{\v{z}} and
Marci{\'n}czuk, Micha{\l} and
Nakov, Preslav and
Piskorski, Jakub and
Pivovarova, Lidia and
{\v{S}}najder, Jan and
Steinberger, Josef and
Yangarber, Roman",
booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3705",
doi = "10.18653/v1/W19-3705",
pages = "35--43",
abstract = "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.",
}
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%0 Conference Proceedings
%T AGRR 2019: Corpus for Gapping Resolution in Russian
%A Ponomareva, Maria
%A Droganova, Kira
%A Smurov, Ivan
%A Shavrina, Tatiana
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F ponomareva-etal-2019-agrr
%X 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.
%R 10.18653/v1/W19-3705
%U https://aclanthology.org/W19-3705
%U https://doi.org/10.18653/v1/W19-3705
%P 35-43
Markdown (Informal)
[AGRR 2019: Corpus for Gapping Resolution in Russian](https://aclanthology.org/W19-3705) (Ponomareva et al., BSNLP 2019)
ACL
- Maria Ponomareva, Kira Droganova, Ivan Smurov, and Tatiana Shavrina. 2019. AGRR 2019: Corpus for Gapping Resolution in Russian. In Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pages 35–43, Florence, Italy. Association for Computational Linguistics.