@inproceedings{rebiai-etal-2019-scia,
title = "{SCIA} at {S}em{E}val-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning",
author = "Rebiai, Zinedine and
Andersen, Simon and
Debrenne, Antoine and
Lafargue, Victor",
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://aclanthology.org/S19-2051/",
doi = "10.18653/v1/S19-2051",
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 (F1{\ensuremath{\mu}}) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1{\ensuremath{\mu}} of 0.7324 on the test dataset."
}
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%0 Conference Proceedings
%T SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning
%A Rebiai, Zinedine
%A Andersen, Simon
%A Debrenne, Antoine
%A Lafargue, Victor
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F rebiai-etal-2019-scia
%X 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 (F1\ensuremathμ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1\ensuremathμ of 0.7324 on the test dataset.
%R 10.18653/v1/S19-2051
%U https://aclanthology.org/S19-2051/
%U https://doi.org/10.18653/v1/S19-2051
%P 297-301
Markdown (Informal)
[SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning](https://aclanthology.org/S19-2051/) (Rebiai et al., SemEval 2019)
ACL