@InProceedings{bansal-nagel-soloveva:2019:S19-2,
  author    = {Bansal, Himanshu  and  Nagel, Daniel  and  Soloveva, Anita},
  title     = {HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {622--627},
  abstract  = {This paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 - Task 6: "OffensEval: Identifying and Categorizing Offensive Language in Social Media". We participated in all the three sub-tasks: Sub-task A - "Offensive language identification", sub-task B - "Automatic categorization of offense types" and sub-task C - "Offense target identification". As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.},
  url       = {http://www.aclweb.org/anthology/S19-2111}
}