Automated correction improves named entity recognition in low-resource languages
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages
Computation and LanguageArtificial Intelligence
Summary
Named Entity Recognition (NER) systems often struggle when the data they learn from has mistakes. The authors created a step-by-step method that automatically fixes errors in these datasets, improving NER results. Their approach uses patterns in the data and confidence checks to clean up annotations. They also checked how well large language models can do NER in languages with limited data.
What this means in practice
- •For language technology developers: Improve NER training datasets by automatically fixing annotation errors in low-resource languages, enhancing system accuracy.
- •For multilingual ai service teams: Deploy large language models for better named entity recognition on languages with limited annotated data.
Authors
Toqeer Ehsan, Thamar Solorio
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.