# CLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Dataset

arXiv: https://arxiv.org/abs/2408.12362 · HTML: https://mlsanity.com/paper/2408.12362/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Detecting Label Errors in Token Classification Data](https://mlsanity.com/paper/2210.03920/) (2022)
- [CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset](https://mlsanity.com/paper/2310.16225/) (2023)

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

- [Tahakom LLM Guidelines and Recipes: From Pre-training Data to an Arabic LLM](https://mlsanity.com/paper/2510.13481/) (2025)
- [AraSSM: A bidirectional state-space encoder for Arabic masked language modeling](https://mlsanity.com/paper/2608.08256/) (2026)
- [A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages](https://mlsanity.com/paper/2609.18739/) (2026)

## Abstract (arXiv)

Label errors are a common issue in machine learning datasets, particularly for tasks such as Named Entity Recognition. Such label errors might hurt model training, affect evaluation results, and lead to an inaccurate assessment of model performance. In this study, we dived deep into one of the widely adopted Arabic NER benchmark datasets (ANERcorp) and found a significant number of annotation errors, missing labels, and inconsistencies. Therefore, in this study, we conducted empirical research to understand these errors, correct them and propose a cleaner version of the dataset named CLEANANERCorp. CLEANANERCorp will serve the research community as a more accurate and consistent benchmark.