# Cross-lingual Named Entity Corpus for Slavic Languages

arXiv: https://arxiv.org/abs/2404.00482 · HTML: https://mlsanity.com/paper/2404.00482/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [NEREL: A Russian Dataset with Nested Named Entities, Relations and Events](https://mlsanity.com/paper/2108.13112/) (2021)
- [RuNNE-2022 Shared Task: Recognizing Nested Named Entities](https://mlsanity.com/paper/2205.11159/) (2022)

## Built on by (most cited first, 2 of 2)

- [OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages](https://mlsanity.com/paper/2412.09587/) (2024)
- [Dataset Scarcity Limits Robust Evaluation of Multilingual Embedding Models: A Case Study of Slavic Languages](https://mlsanity.com/paper/2608.24477/) (2026)

## Abstract (arXiv)

This paper presents a corpus manually annotated with named entities for six Slavic languages - Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017-2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5 017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits - single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models - XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.