NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links.
This paper describes NEREL—a Russian news dataset suited for three tasks: nested named entity recognition, relation extraction, and entity linking. Compared to flat entities, nested named entities provide a richer and more complete annotation while also increasing the coverage of relations annotatio...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 2; pp. 547 - 584 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178064680&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178064680 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2024 vid: 58 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 178064680 10.1007/s10579-023-09674-z ppf: 547 ppct: 37 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.5MB tig: atl: NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links. aug: au: Loukachevitch, Natalia Artemova, Ekaterina Batura, Tatiana Braslavski, Pavel Ivanov, Vladimir Manandhar, Suresh Pugachev, Alexander Rozhkov, Igor Shelmanov, Artem Tutubalina, Elena Yandutov, Alexey affil: https://ror.org/010pmpe69 Lomonosov Moscow State University, Moscow, Russia ISP RAS Research Center for Trusted Artificial Intelligence, Moscow, Russia HSE University, Moscow, Russia https://ror.org/04t2ss102 Novosibirsk State University, Novosibirsk, Russia https://ror.org/01dc8vg74 A.P. Ershov Institute of Informatics Systems, Novosibirsk, Russia https://ror.org/00hs7dr46 Ural Federal University, Yekaterinburg, Russia https://ror.org/02b7jh107 Innopolis University, Innopolis, Russia https://ror.org/021xhya68 Madan Bhandari University of Science and Technology, Chitlang, Nepal https://ror.org/014a87f14 AIRI, Moscow, Russia https://ror.org/0258gkt32 Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE Sber AI, Moscow, Russia su: Data mining Annotations News websites sug: subj: Data mining Annotations News websites keyword: 68T35 68T50 Entity linking Named entity recognition Nested entities Nested relations Relation extraction ab: This paper describes NEREL—a Russian news dataset suited for three tasks: nested named entity recognition, relation extraction, and entity linking. Compared to flat entities, nested named entities provide a richer and more complete annotation while also increasing the coverage of relations annotation and entity linking. Relations between nested named entities may cross entity boundaries to connect to shorter entities nested within longer ones, which makes it harder to detect such relations. NEREL is currently the largest Russian dataset annotated with entities and relations: it comprises 29 named entity types and 49 relation types. At the time of writing, the dataset contains 56 K named entities and 39 K relations annotated in 933 person-oriented news articles. NEREL is annotated with relations at three levels: (1) within nested named entities, (2) within sentences, and (3) with relations crossing sentence boundaries. We provide benchmark evaluation of current state-of-the-art methods in all three tasks. The dataset is freely available at https://github.com/nerel-ds/NEREL. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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