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To achieve state-of-the-art performance, one still needs to train NER modelson large-scale, high-quality annotated data, an asset that is both costly andtime-intensive to accumulate. In contrast, real-world applications often resortto massive low-quality labeled data through non-expert annotators viacrowdsourcing and external knowledge bases via distant supervision as acost-effective alternative. However, these annotation methods result in noisylabels, which in turn lead to a notable decline in performance. Hence, wepropose to denoise the noisy NER data with guidance from a small set of cleaninstances. Along with the main NER model we train a discriminator model and useits outputs to recalibrate the sample weights. The discriminator is capable ofdetecting both span and category errors with different discriminative prompts.Results on public crowdsourcing and distant supervision datasets show that theproposed method can consistently improve performance with a small guidance set.

Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances