  Applications such as machine translation, speech recognition, and information
retrieval require efficient handling of noun compounds as they are one of the
possible sources for out-of-vocabulary (OOV) words. In-depth processing of noun
compounds requires not only splitting them into smaller components (or even
roots) but also the identification of instances that should remain unsplitted
as they are of idiomatic nature. We develop a two-fold deep learning-based
approach of noun compound splitting and idiomatic compound detection for the
German language that we train using a newly collected corpus of annotated
German compounds. Our neural noun compound splitter operates on a sub-word
level and outperforms the current state of the art by about 5%.
