@inproceedings{almatarneh-etal-2019-citius,
    title = "{C}i{TIUS}-{COLE} at {S}em{E}val-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets",
    author = "Almatarneh, Sattam  and
      Gamallo, Pablo  and
      Pena, Francisco J. Ribadas",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2068/",
    doi = "10.18653/v1/S19-2068",
    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"
}