  This paper presents a novel framework, MGNER, for Multi-Grained Named Entity
Recognition where multiple entities or entity mentions in a sentence could be
non-overlapping or totally nested. Different from traditional approaches
regarding NER as a sequential labeling task and annotate entities
consecutively, MGNER detects and recognizes entities on multiple granularities:
it is able to recognize named entities without explicitly assuming
non-overlapping or totally nested structures. MGNER consists of a Detector that
examines all possible word segments and a Classifier that categorizes entities.
In addition, contextual information and a self-attention mechanism are utilized
throughout the framework to improve the NER performance. Experimental results
show that MGNER outperforms current state-of-the-art baselines up to 4.4% in
terms of the F1 score among nested/non-overlapping NER tasks.

Computation and Language