@inproceedings{loukachevitch-etal-2022-entity,
title = "Entity Linking over Nested Named Entities for {R}ussian",
author = "Loukachevitch, Natalia and
Braslavski, Pavel and
Ivanov, Vladimir and
Batura, Tatiana and
Manandhar, Suresh and
Shelmanov, Artem and
Tutubalina, Elena",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.474",
pages = "4458--4466",
abstract = "In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction. The NEREL collection is currently the largest Russian dataset annotated with entities and relations. It includes 933 news texts with annotation of 29 entity types and 49 relation types. The paper describes the main design principles behind NEREL{'}s entity linking annotation, provides its statistics, and reports evaluation results for several entity linking baselines. To date, 38,152 entity mentions in 933 documents are linked to Wikidata. The NEREL dataset is publicly available.",
}
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%0 Conference Proceedings
%T Entity Linking over Nested Named Entities for Russian
%A Loukachevitch, Natalia
%A Braslavski, Pavel
%A Ivanov, Vladimir
%A Batura, Tatiana
%A Manandhar, Suresh
%A Shelmanov, Artem
%A Tutubalina, Elena
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F loukachevitch-etal-2022-entity
%X In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction. The NEREL collection is currently the largest Russian dataset annotated with entities and relations. It includes 933 news texts with annotation of 29 entity types and 49 relation types. The paper describes the main design principles behind NEREL’s entity linking annotation, provides its statistics, and reports evaluation results for several entity linking baselines. To date, 38,152 entity mentions in 933 documents are linked to Wikidata. The NEREL dataset is publicly available.
%U https://aclanthology.org/2022.lrec-1.474
%P 4458-4466
Markdown (Informal)
[Entity Linking over Nested Named Entities for Russian](https://aclanthology.org/2022.lrec-1.474) (Loukachevitch et al., LREC 2022)
ACL
- Natalia Loukachevitch, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Suresh Manandhar, Artem Shelmanov, and Elena Tutubalina. 2022. Entity Linking over Nested Named Entities for Russian. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4458–4466, Marseille, France. European Language Resources Association.