UkraiNER: A New Corpus and Annotation Scheme Towards Comprehensive Entity Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AAJNW2MLV" target="_blank" >RIV/00216208:11320/25:AJNW2MLV - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195968083&partnerID=40&md5=f137210b16c92b9d973b83933fe54cdb" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195968083&partnerID=40&md5=f137210b16c92b9d973b83933fe54cdb</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

UkraiNER: A New Corpus and Annotation Scheme Towards Comprehensive Entity Recognition

Popis výsledku v původním jazyce

Named entity recognition as it is traditionally envisioned excludes in practice a significant part of the entities of potential interest for real-word applications: nested, discontinuous, non-named entities. Despite various attempts to broaden their coverage, subsequent annotation schemes have achieved little adoption in the literature and the most restrictive variant of NER remains the default. This is partly due to the complexity of those annotations and their format. In this paper, we introduce a new annotation scheme that offers higher comprehensiveness while preserving simplicity, together with an annotation tool to implement that scheme. We also release the corpus UkraiNER, comprised of 10,000 French sentences in the geopolitical news domain and manually annotated with comprehensive entity recognition. Our baseline experiments on UkraiNER provide a first point of comparison to facilitate future research (82 F1 for comprehensive entity recognition, 87 F1 when focusing on traditional nested NER), as well as various insights on the composition and challenges that this corpus presents for state-of-the-art named entity recognition models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Název v anglickém jazyce

UkraiNER: A New Corpus and Annotation Scheme Towards Comprehensive Entity Recognition

Popis výsledku anglicky

Named entity recognition as it is traditionally envisioned excludes in practice a significant part of the entities of potential interest for real-word applications: nested, discontinuous, non-named entities. Despite various attempts to broaden their coverage, subsequent annotation schemes have achieved little adoption in the literature and the most restrictive variant of NER remains the default. This is partly due to the complexity of those annotations and their format. In this paper, we introduce a new annotation scheme that offers higher comprehensiveness while preserving simplicity, together with an annotation tool to implement that scheme. We also release the corpus UkraiNER, comprised of 10,000 French sentences in the geopolitical news domain and manually annotated with comprehensive entity recognition. Our baseline experiments on UkraiNER provide a first point of comparison to facilitate future research (82 F1 for comprehensive entity recognition, 87 F1 when focusing on traditional nested NER), as well as various insights on the composition and challenges that this corpus presents for state-of-the-art named entity recognition models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

16941-16952

Název nakladatele

European Language Resources Association (ELRA)

Místo vydání

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Místo konání akce

Torino, Italia

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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