  This study improves the performance of neural named entity recognition by a
margin of up to 11% in F-score on the example of a low-resource language like
German, thereby outperforming existing baselines and establishing a new
state-of-the-art on each single open-source dataset. Rather than designing
deeper and wider hybrid neural architectures, we gather all available resources
and perform a detailed optimization and grammar-dependent morphological
processing consisting of lemmatization and part-of-speech tagging prior to
exposing the raw data to any training process. We test our approach in a
threefold monolingual experimental setup of a) single, b) joint, and c)
optimized training and shed light on the dependency of downstream-tasks on the
size of corpora used to compute word embeddings.
