Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy. Previous works have shown that significant improvements can be reached by injecting information about the confusion between clean and noisy labels in this additional training data into the classifier training... (read more)

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