  Named Entity Recognition (NER) has greatly advanced by the introduction of
deep neural architectures. However, the success of these methods depends on
large amounts of training data. The scarcity of publicly-available
human-labeled datasets has resulted in limited evaluation of existing NER
systems, as is the case for Danish. This paper studies the effectiveness of
cross-lingual transfer for Danish, evaluates its complementarity to limited
gold data, and sheds light on performance of Danish NER.
