Abstract
This paper presents a novel framework,
MGNER, for Multi-Grained Named Entity
Recognition where multiple entities or entity mentions in a sentence could be nonoverlapping 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.