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

arXiv:2609.18739 · cs.CL, cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-16

Updated: 2026-09-16

Comments: Accepted to Findings of EACL 2026

Code: https://github.com/toqeerehsan/low-res_ne_correction

License: http://creativecommons.org/licenses/by/4.0/

The gist: 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.

Terminology

Abstract

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.

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