  Recent attempts to ingest external knowledge into neural models for
named-entity recognition (NER) have exhibited mixed results. In this work, we
present GazSelfAttn, a novel gazetteer embedding approach that uses
self-attention and match span encoding to build enhanced gazetteer embeddings.
In addition, we demonstrate how to build gazetteer resources from the open
source Wikidata knowledge base. Evaluations on CoNLL-03 and Ontonotes 5
datasets, show F1 improvements over baseline model from 92.34 to 92.86 and
89.11 to 89.32 respectively, achieving performance comparable to large
state-of-the-art models.
