@InProceedings{almatarneh-gamallo-pena:2019:S19-2,
  author    = {Almatarneh, Sattam  and  Gamallo, Pablo  and  Pena, Francisco J. Ribadas},
  title     = {CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {387--390},
  abstract  = {This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classi- fication where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguis- tic features to improve the classifier’s perfor- mance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency- Inverse Document Frequency (TF-IDF) repre- sentation. The system performance reaches about 81\% F1 when it is applied to the training dataset, but its F1 drops to 36\% on the official test dataset for the English and 64\% for the Spanish language concerning the hate speech class},
  url       = {http://www.aclweb.org/anthology/S19-2068}
}