NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models. It is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are:

Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances
Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged
Skewed label distribution (typical for Named Entity Recognition tasks)
For some label sets: noise level higher than the true label probability
Sequential dependencies between the labels

For more details on the dataset and its creation process, please refer to our publication https://ojs.aaai.org/index.php/AAAI/article/view/16938 (published at AAAI'21).

phucdev/noisyner

phucdev/
noisyner