@InProceedings{rebiai-EtAl:2019:S19-2,
  author    = {Rebiai, Zinedine  and  Andersen, Simon  and  Debrenne, Antoine  and  Lafargue, Victor},
  title     = {SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {297--301},
  abstract  = {In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.},
  url       = {http://www.aclweb.org/anthology/S19-2051}
}