@InProceedings{torres-vaca:2019:S19-2,
  author    = {Torres, Johnny  and  Vaca, Carmen},
  title     = {JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional 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     = {657--661},
  abstract  = {In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words' representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.},
  url       = {http://www.aclweb.org/anthology/S19-2117}
}