@InProceedings{kumar-EtAl:2019:S19-2,
  author    = {Kumar, Ritesh  and  Bhanodai, Guggilla  and  Pamula, Rajendra  and  Chennuru, Maheswara Reddy},
  title     = {bhanodaig at SemEval-2019 Task 6: Categorizing Offensive Language in social media},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {547--550},
  abstract  = {This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.},
  url       = {http://www.aclweb.org/anthology/S19-2098}
}