Entity Linking over Nested Named Entities for Russian

被引：0

作者：

Loukachevitch, Natalia ^{[1
]}

Braslavski, Pavel ^{[2
,3
]}

Ivanov, Vladimir ^{[4
]}

Batura, Tatiana ^{[5
,6
]}

Manandhar, Suresh ^{[9
]}

Shelmanov, Artem ^{[1
,7
]}

Tutubalina, Elena ^{[3
,8
]}

机构：

[1] Lomonosov Moscow State Univ, Moscow, Russia

[2] Ural Fed Univ, Ekaterinburg, Russia

[3] HSE Univ, Moscow, Russia

[4] Innopolis Univ, Innopolis, Russia

[5] Novosibirsk State Univ, Novosibirsk, Russia

[6] Ershov Inst Informat Syst, Novosibirsk, Russia

[7] Artificial Intelligence Res Inst, Moscow, Russia

[8] Sber AI, Moscow, Russia

[9] Madan Bhandari Univ Sci & Technol Dev Board, Kathmandu, Nepal

来源：

LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2022年

基金：

俄罗斯科学基金会;

关键词：

information extraction; entity linking; nested named entities; Russian language; DISAMBIGUATION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 (Loukachevitch et al., 2021) is currently the largest Russian dataset annotated with entities and relations. 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: https://github.com/nerel-ds/NEREL.

引用

页码：4458 / 4466

页数：9

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