I would like to train a neural network for named entity recognition to tag an unlabeled dataset of texts. The generated labels will then be checked via a crowdsourcing platform. The goal is to annotate the dataset. Therefore, the neural net should find all possible entities in the text, i.e. have high recall rather than precision.
What would be the best way to train a neural network for high recall, i.e. assigning lower cost to false positives than to false negatives? Could the loss function be changed from negative log likelihood to something else to encourage high recall?