The strategy of finding entities is to first generate all the word segments as entity proposals and then estimate the probability of each proposal as being an entity or not.
in this work, we propose a novel neural framework named MGNER for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.