@inproceedings{WiegandChikobavaRuppenhofer2019,
  author    = {Michael Wiegand and Margarita Chikobava and Josef Ruppenhofer},
  title     = {A Supervised learning approach for the extraction of opinion sources and targets from German text},
  series = {Preliminary proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019), October 9 – 11, 2019 at Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg},
  publisher = {German Society for Computational Linguistics \& Language Technology und Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg},
  address   = {M{\"u}nchen [u.a.]},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-93218},
  pages     = {10 -- 19},
  year      = {2019},
  abstract  = {We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.},
  language  = {en}
}