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arXiv AI By Toqeer Ehsan, Thamar Solorio

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

Read the original on arXiv AI →

The paper introduces a scalable, multi-step framework designed to improve the quality of Named Entity Recognition (NER) annotations, particularly in low-resource languages. It employs a frequency-based iterative approach that combines self‑training with a dual‑threshold mechanism to increase inference confidence. Experiments on various NER datasets show notable performance gains over the original data, and the study also investigates the use of generative Large Language Models for NER tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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