@inproceedings{koeper16:_improv_zero_shot_learn_german,
  abstract = {Recent models in distributional semantics consider derivational patterns (e.g., use → use+ful) as the result of a compositional process, where base term and affix are combined. We exploit such models for German particle verbs (PVs), and focus on the task of learning a mapping function between base verbs and particle verbs. Our models apply particle-verb motivated training-space restrictions relying on nearest neighbors, as well as recent advances from zero- shot-learning. The models improve the mapping between base terms and derived terms for a new PV derivation dataset, and also across existing derivation datasets for German and English.},
  added-at = {2017-04-03T19:28:28.000+0200},
  address = {Berlin, Germany},
  author = {Köper, Maximilian and {Schulte im Walde}, Sabine and Kisselew, Max and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/295025827d4e1b939ab172cd4e5965d4e/sp},
  booktitle = {Proceedings of STARSEM},
  interhash = {0ac2933ed46976e8c1e2290de28249b6},
  intrahash = {95025827d4e1b939ab172cd4e5965d4e},
  keywords = {conference myown},
  timestamp = {2017-04-03T17:28:32.000+0200},
  title = {Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling},
  url = {http://www.aclweb.org/anthology/S16-2010.pdf},
  year = 2016
}