AI RESEARCH
Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark
arXiv CS.CL
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ArXi:2604.12744v1 Announce Type: new While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most languages to interrogate these assumptions remain scarce. The Universal NER project, now entering its fourth year, is dedicated to building gold-standard multilingual Named Entity Recognition (NER) benchmark datasets. Inspired by existing massively multilingual efforts for other core NLP tasks (e.g., Universal Dependencies), the project uses a general.