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

arXiv: https://arxiv.org/abs/2311.09122 · HTML: https://mlsanity.com/paper/2311.09122/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 69 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [UniMorph 2.0: Universal Morphology](https://mlsanity.com/paper/1810.11101/) (2018)
- [Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language](https://mlsanity.com/paper/2004.12440/) (2020)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data](https://mlsanity.com/paper/2007.07683/) (2020)
- [DaN+: Danish Nested Named Entities and Lexical Normalization](https://mlsanity.com/paper/2105.11301/) (2021)
- [NorNE: Annotating Named Entities for Norwegian](https://mlsanity.com/paper/1911.12146/) (2019)
- [Toward More Meaningful Resources for Lower-resourced Languages](https://mlsanity.com/paper/2202.12288/) (2022)
- [Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish](https://mlsanity.com/paper/2003.02931/) (2020)
- [Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language](https://mlsanity.com/paper/2306.14866/) (2023)
- [UNER: Universal Named-Entity RecognitionFramework](https://mlsanity.com/paper/2010.12406/) (2020)
- [Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations](https://mlsanity.com/paper/1910.10893/) (2019)

## Built on by (most cited first, 26 of 26)

- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs](https://mlsanity.com/paper/2502.14830/) (2025)
- [Retrofitting Large Language Models with Dynamic Tokenization](https://mlsanity.com/paper/2411.18553/) (2024)
- [Should We Still Pretrain Encoders with Masked Language Modeling?](https://mlsanity.com/paper/2507.00994/) (2025)
- [Multilingual Pretraining for Pixel Language Models](https://mlsanity.com/paper/2505.21265/) (2025)
- [M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2502.11824/) (2025)
- [skLEP: A Slovak General Language Understanding Benchmark](https://mlsanity.com/paper/2506.21508/) (2025)
- [Learning Dynamics of Meta-Learning in Small Model Pretraining](https://mlsanity.com/paper/2508.02189/) (2025)
- [FilBench: Can LLMs Understand and Generate Filipino?](https://mlsanity.com/paper/2508.03523/) (2025)
- [Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages](https://mlsanity.com/paper/2509.02160/) (2025)
- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation](https://mlsanity.com/paper/2504.08792/) (2025)
- [Strategies for Span Labeling with Large Language Models](https://mlsanity.com/paper/2601.16946/) (2026)
- [NameTag 3: A Tool and a Service for Multilingual/Multitagset NER](https://mlsanity.com/paper/2506.05949/) (2025)
- [SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval](https://mlsanity.com/paper/2410.21501/) (2024)
- [Improving Low-Resource Sequence Labeling with Knowledge Fusion and Contextual Label Explanations](https://mlsanity.com/paper/2501.19093/) (2025)
- [Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?](https://mlsanity.com/paper/2505.16814/) (2025)
- [What Matters When Building Universal Multilingual Named Entity Recognition Models?](https://mlsanity.com/paper/2601.06347/) (2026)
- [Efficient Multilingual Name Type Classification Using Convolutional Networks](https://mlsanity.com/paper/2601.11090/) (2026)
- [Avey-B](https://mlsanity.com/paper/2602.15814/) (2026)
- [CzechTopic: A Benchmark for Zero-Shot Topic Localization in Historical Czech Documents](https://mlsanity.com/paper/2603.03884/) (2026)
- [Just Use XML: Revisiting Joint Translation and Label Projection](https://mlsanity.com/paper/2603.12021/) (2026)
- [Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2604.12744/) (2026)
- [BenCSSmark: Making the Social Sciences Count in LLM Research](https://mlsanity.com/paper/2605.04886/) (2026)
- [Pad=artha: Ontology-Grounded Fine-Grained NER Benchmark for Classical Sanskrit](https://mlsanity.com/paper/2608.29324/) (2026)
- [A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages](https://mlsanity.com/paper/2609.18739/) (2026)

## Abstract (arXiv)

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.