# MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition

arXiv: https://arxiv.org/abs/2310.13213 · HTML: https://mlsanity.com/paper/2310.13213/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [ner and pos when nothing is capitalized](https://mlsanity.com/paper/1903.11222/) (2019)

## Built on by (most cited first, 3 of 3)

- [Refining Wikidata Taxonomy using Large Language Models](https://mlsanity.com/paper/2409.04056/) (2024)
- [Surrogate Substitution Preserves PHI Detectability: A Multi-Detector Equivalence Study](https://mlsanity.com/paper/2608.03172/) (2026)
- [UltraBERT: Beyond the Limit of BERT Pre-Training](https://mlsanity.com/paper/2610.15028/) (2026)

## Abstract (arXiv)

We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This dataset aims to tackle the following practical challenges in NER: (i) effective handling of fine-grained classes that include complex entities like movie titles, and (ii) performance degradation due to noise generated from typing mistakes or OCR errors. The dataset is compiled from open resources like Wikipedia and Wikidata, and is publicly available. Evaluation based on the XLM-RoBERTa baseline highlights the unique challenges posed by MULTICONER V2: (i) the fine-grained taxonomy is challenging, where the scores are low with macro-F1=0.63 (across all languages), and (ii) the corruption strategy significantly impairs performance, with entity corruption resulting in 9% lower performance relative to non-entity corruptions across all languages. This highlights the greater impact of entity noise in contrast to context noise.