@InProceedings{perell-EtAl:2019:S19-2,
  author    = {Perelló, Carlos  and  Tomás, David  and  Garcia-Garcia, Alberto  and  Garcia-Rodriguez, Jose  and  Camacho-Collados, Jose},
  title     = {UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {508--513},
  abstract  = {This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate. The purpose of this work is to build a strong baseline for hate speech detection, using a traditional machine learning approach with standard textual features, which could serve in a near future as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification). Despite its simplicity, our system obtained a remarkable F1-score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.},
  url       = {http://www.aclweb.org/anthology/S19-2091}
}