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Communication Dans Un Congrès Année : 2024

Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark

Résumé

We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 19 datasets annotated with named entities in a cross-lingual consistent schema across 13 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public.
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hal-04630484 , version 1 (01-07-2024)

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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, et al.. Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark. 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2024, Mexico city, Mexico. ⟨10.7910/DVN/GQ8HDL⟩. ⟨hal-04630484⟩
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