mlsanityData to 14 Oct 2026

A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages

arXiv 2609.18739 ↗First posted 16 Sep 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Poor quality or noisy annotations in Named Entity Recognition (NER), as in any other NLP task, make it challenging to achieve state-of-the-art performance. In this paper, we present a multi-step framework to enhance the annotation quality of NER datasets by employing automated techniques. We propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence. Experimental evaluations on different NER datasets demonstrate significant improvements in NER performance with respect to the original datasets. This work further explores the potential of generative Large Language Models (LLMs) to perform NER for low-resource languages.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 16 Sep 2026.

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References · 21

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
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  9. 2023How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Taskscited by 58
  10. 2023Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmarkcited by 26
  11. 2023Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologiescited by 16
  12. 2022Detecting Label Errors in Token Classification Datacited by 12
9 more
  1. 2023CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Datasetcited by 11
  2. 2024Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Deliverycited by 5
  3. 2018Urdu Word Segmentation using Conditional Random Fields (CRFs)cited by 4
  4. 2024Annotation Errors and NER: A Study with OntoNotes 5.0cited by 4
  5. 2024CLEANANERCorp: Identifying and Correcting Incorrect Labels in the ANERcorp Datasetcited by 3
  6. 2024CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 Englishcited by 2
  7. 2024An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domainscited by 2
  8. 2025Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentationcited by 2
  9. 2020Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attentioncited by 1

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