@inproceedings{loukachevitch-etal-2021-nerel,
title = "{NEREL}: A {R}ussian Dataset with Nested Named Entities, Relations and Events",
author = "Loukachevitch, Natalia and
Artemova, Ekaterina and
Batura, Tatiana and
Braslavski, Pavel and
Denisov, Ilia and
Ivanov, Vladimir and
Manandhar, Suresh and
Pugachev, Alexander and
Tutubalina, Elena",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.100",
pages = "876--885",
abstract = "In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via \url{https://github.com/nerel-ds/NEREL}.",
}
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<abstract>In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via https://github.com/nerel-ds/NEREL.</abstract>
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%0 Conference Proceedings
%T NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
%A Loukachevitch, Natalia
%A Artemova, Ekaterina
%A Batura, Tatiana
%A Braslavski, Pavel
%A Denisov, Ilia
%A Ivanov, Vladimir
%A Manandhar, Suresh
%A Pugachev, Alexander
%A Tutubalina, Elena
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F loukachevitch-etal-2021-nerel
%X In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via https://github.com/nerel-ds/NEREL.
%U https://aclanthology.org/2021.ranlp-1.100
%P 876-885
Markdown (Informal)
[NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://aclanthology.org/2021.ranlp-1.100) (Loukachevitch et al., RANLP 2021)
ACL
- Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Ilia Denisov, Vladimir Ivanov, Suresh Manandhar, Alexander Pugachev, and Elena Tutubalina. 2021. NEREL: A Russian Dataset with Nested Named Entities, Relations and Events. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 876–885, Held Online. INCOMA Ltd..