  For named entity recognition (NER), bidirectional recurrent neural networks
became the state-of-the-art technology in recent years. Competing approaches
vary with respect to pre-trained word embeddings as well as models for
character embeddings to represent sequence information most effectively. For
NER in German language texts, these model variations have not been studied
extensively. We evaluate the performance of different word and character
embeddings on two standard German datasets and with a special focus on
out-of-vocabulary words. With F-Scores above 82% for the GermEval'14 dataset
and above 85% for the CoNLL'03 dataset, we achieve (near) state-of-the-art
performance for this task. We publish several pre-trained models wrapped into a
micro-service based on Docker to allow for easy integration of German NER into
other applications via a JSON API.
