@InProceedings{do-rehbein-frank:2017:SCLeM,
  author    = {Do, Bich-Ngoc  and  Rehbein, Ines  and  Frank, Anette},
  title     = {What do we need to know about an unknown word when parsing German},
  booktitle = {Proceedings of the First Workshop on Subword and Character Level Models in NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {117--123},
  abstract  = {We propose a new type of subword embedding designed to provide more information
	about unknown compounds, a major source for OOV words in German. We present an
	extrinsic evaluation where we use the compound embeddings as input to a neural
	dependency parser and compare the results to the ones obtained with other types
	of embeddings. Our evaluation shows that adding compound embeddings yields a
	significant improvement of 2% LAS over using word embeddings when no POS
	information is available. When adding POS embeddings to the input, however,
	the effect levels out. This suggests that it is not the missing information
	about the semantics of the unknown words that causes problems for parsing
	German, but the lack of morphological information for unknown words. To augment
	our evaluation, we also test the new embeddings in a language modelling task
	that requires both syntactic and semantic information.},
  url       = {http://www.aclweb.org/anthology/W17-4117}
}