Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ANFDLZVS8" target="_blank" >RIV/00216208:11320/25:NFDLZVS8 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189941153&partnerID=40&md5=9748a22bef9f828d9e6ff21f9859bed8" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189941153&partnerID=40&md5=9748a22bef9f828d9e6ff21f9859bed8</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations

Popis výsledku v původním jazyce

Named Entity Recognition (NER) is a key information extraction task with a long-standing tradition. While recent studies address and aim to correct annotation errors via re-labeling efforts, little is known about the sources of human label variation, such as text ambiguity, annotation error, or guideline divergence. This is especially the case for high-quality datasets and beyond English CoNLL03. This paper studies disagreements in expert-annotated named entity datasets for three languages: English, Danish, and Bavarian. We show that text ambiguity and artificial guideline changes are dominant factors for diverse annotations among high-quality revisions. We survey student annotations on a subset of difficult entities and substantiate the feasibility and necessity of manifold annotations for understanding named entity ambiguities from a distributional perspective. © 2024 UnImplicit 2024 - 3rd Workshop on Understanding Implicit and Underspecified Language, Proceedings of the Workshop. All rights reserved.

Název v anglickém jazyce

Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations

Popis výsledku anglicky

Named Entity Recognition (NER) is a key information extraction task with a long-standing tradition. While recent studies address and aim to correct annotation errors via re-labeling efforts, little is known about the sources of human label variation, such as text ambiguity, annotation error, or guideline divergence. This is especially the case for high-quality datasets and beyond English CoNLL03. This paper studies disagreements in expert-annotated named entity datasets for three languages: English, Danish, and Bavarian. We show that text ambiguity and artificial guideline changes are dominant factors for diverse annotations among high-quality revisions. We survey student annotations on a subset of difficult entities and substantiate the feasibility and necessity of manifold annotations for understanding named entity ambiguities from a distributional perspective. © 2024 UnImplicit 2024 - 3rd Workshop on Understanding Implicit and Underspecified Language, Proceedings of the Workshop. All rights reserved.

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

UnImplicit - Workshop Underst. Implicit Underspecified Lang., Proc. Workshop

ISBN

979-889176083-7

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

73-81

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

St. Julian's

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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