Is it possible to use an arbitrary machine learning tool instead of SpaCy during active learning. For example, if we are ultimately wanting to create a Stanford CoreNLP model can we use a python wrapper and then use CoreNLP during active learning phase?
Yes, you should be able to implement this with a similar approach to the one I outline here:
One thing that's important to keep in mind for active learning-powered NER is that your model needs to handle updates with sparse labels. For example, if you receive an accepted entity annotation back, your model shouldn't accidentally assume that all other tags are
O (outside an entity) and instead treat them as missing values.