  Most existing named entity recognition (NER) approaches are based on sequence
labeling models, which focus on capturing the local context dependencies.
However, the way of taking one sentence as input prevents the modeling of
non-sequential global context, which is useful especially when local context
information is limited or ambiguous. To this end, we propose a model called
Global Context enhanced Document-level NER (GCDoc) to leverage global
contextual information from two levels, i.e., both word and sentence. At
word-level, a document graph is constructed to model a wider range of
dependencies between words, then obtain an enriched contextual representation
for each word via graph neural networks (GNN). To avoid the interference of
noise information, we further propose two strategies. First we apply the
epistemic uncertainty theory to find out tokens whose representations are less
reliable, thereby helping prune the document graph. Then a selective auxiliary
classifier is proposed to effectively learn the weight of edges in document
graph and reduce the importance of noisy neighbour nodes. At sentence-level,
for appropriately modeling wider context beyond single sentence, we employ a
cross-sentence module which encodes adjacent sentences and fuses it with the
current sentence representation via attention and gating mechanisms. Extensive
experiments on two benchmark NER datasets (CoNLL 2003 and Ontonotes 5.0 English
dataset) demonstrate the effectiveness of our proposed model. Our model reaches
F1 score of 92.22 (93.40 with BERT) on CoNLL 2003 dataset and 88.32 (90.49 with
BERT) on Ontonotes 5.0 dataset, achieving new state-of-the-art performance.
