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arxiv 2311.09122 v3 pith:GPNPCGFL submitted 2023-11-15 cs.CL

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

Stephen Mayhew , Terra Blevins , Shuheng Liu , Marek \v{S}uppa , Hila Gonen , Joseph Marvin Imperial , B\"orje F. Karlsson , Peiqin Lin
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Nikola Ljube\v{s}i\'c LJ Miranda Barbara Plank Arij Riabi Yuval Pinter
This is my paper
classification cs.CL
keywords unerconsistentcross-lingualgold-standardlanguagesmultilingualnameduniversal
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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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