After tagging the sentences with the top-level categories, we can convert the FG-NER problem into a simple classification problem (not a sequence labeling problem anymore), thus we can use SVM to classify the extracted entities at the top level into leaf-level categories.
we have a CRF model to tag the input sentences with top-level categories, and several SVM models (each for a top-level category) to classify the entities into the leaf-level categories.