@InProceedings{suseelan-EtAl:2019:S19-2,
  author    = {Suseelan, Angel  and  S, Rajalakshmi  and  B, Logesh  and  S, Harshini  and  B, Geetika  and  S, Dyaneswaran  and  Rajendram, S Milton  and  T T, Mirnalinee},
  title     = {TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {753--758},
  abstract  = {Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google's pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.},
  url       = {http://www.aclweb.org/anthology/S19-2132}
}