With the dictionary, we know that "Joe" is an entity word.
in ordinary supervised learning, we estimate R with the empirical lossR : and update model parameters to learn a classifier f * that minimizesR : Unbiased positive-unlabeled learning (uPU) (du Plessis et al., 2014) aims to estimate R when there are only a set of positive (P) examples and a set of unlabeled (U) examples, which contains both positive and negative examples.