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arXiv · 2311.09122

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

Stephen Mayhew·Terra Blevins·Shuheng Liu·Marek Šuppa·Hila Gonen·Joseph Marvin Imperial·Börje F. Karlsson·Peiqin Lin·Nikola Ljubešić·LJ Miranda·Barbara Plank·Arij Riabi·Yuval Pinter
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Abstract

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 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 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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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riabi, Yuval Pinter. 2023-11-15. Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark. https://arxiv.org/abs/2311.09122

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