Given a set of candidate concepts belonging to generic ontological types (e.g., people or locations), and a set of pre-recognized mentions of entities of these types in the corpus (e.g., Newton, Ontario), we assign the entity to the class whose lexicalization is more frequently entailed in the corpus.
as training set to learn the fine-grained category models, we use all the occurrences of their corresponding expressions in the same corpus (e.g., we collected all occurrences in context of the word scientist to describe the concept scientist).