A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages
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Computer Science > Computation and Language
Title:A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages
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.
| Comments: | Accepted to Findings of EACL 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.18739 [cs.CL] |
| (or arXiv:2609.18739v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.18739
arXiv-issued DOI via DataCite (pending registration)
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