Noisy-Labeled NER with Confidence Estimation
This addresses the problem of noisy data in NER for real-world applications, representing an incremental improvement over existing methods.
The paper tackles named entity recognition (NER) with noisy labels by proposing a method for confidence estimation and calibration, integrating it into a self-training framework. Experiments across four languages and distantly labeled settings show improved performance, though specific numbers are not provided in the abstract.
Recent studies in deep learning have shown significant progress in named entity recognition (NER). Most existing works assume clean data annotation, yet a fundamental challenge in real-world scenarios is the large amount of noise from a variety of sources (e.g., pseudo, weak, or distant annotations). This work studies NER under a noisy labeled setting with calibrated confidence estimation. Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions. We partially marginalize out labels of low confidence with a CRF model. We further propose a calibration method for confidence scores based on the structure of entity labels. We integrate our approach into a self-training framework for boosting performance. Experiments in general noisy settings with four languages and distantly labeled settings demonstrate the effectiveness of our method. Our code can be found at https://github.com/liukun95/Noisy-NER-Confidence-Estimation