@InProceedings{oberstrass-EtAl:2019:S19-2,
  author    = {Oberstrass, Alexander  and  Romberg, Julia  and  Stoll, Anke  and  Conrad, Stefan},
  title     = {HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {628--634},
  abstract  = {We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro $F_1$ of $0.719$. Our approaches for sub-task A and C perform less well but could also deliver promising results.},
  url       = {http://www.aclweb.org/anthology/S19-2112}
}