@InProceedings{indurthi-EtAl:2019:S19-22,
  author    = {Indurthi, Vijayasaradhi  and  Syed, Bakhtiyar  and  Shrivastava, Manish  and  Gupta, Manish  and  Varma, Vasudeva},
  title     = {Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {611--616},
  abstract  = {This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019. We participated in all the three sub-tasks within Task 6. We evaluate multiple sentence embeddings in conjunction with various supervised machine learning algorithms and evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team Fermi's model achieved an F1-score of 64.40\%, 62.00\% and 62.60\% for sub-task A, B and C respectively on the official leaderboard. Our model for sub-task C which uses pre-trained ELMo embeddings for transforming the input and uses SVM (RBF kernel) for training, scored third position on the official leaderboard. Through the paper we provide a detailed description of the approach, as well as the results obtained for the task.},
  url       = {http://www.aclweb.org/anthology/S19-2109}
}