@inproceedings{mohammadi-etal-2019-clac,
title = "{CL}a{C} Lab at {S}em{E}val-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and {SVM}",
author = "Mohammadi, Elham and
Amini, Hessam and
Kosseim, Leila",
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/S19-2023",
doi = "10.18653/v1/S19-2023",
pages = "153--158",
abstract = "This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12{\%}.",
}
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<abstract>This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.</abstract>
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%0 Conference Proceedings
%T CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM
%A Mohammadi, Elham
%A Amini, Hessam
%A Kosseim, Leila
%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 mohammadi-etal-2019-clac
%X This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.
%R 10.18653/v1/S19-2023
%U https://aclanthology.org/S19-2023
%U https://doi.org/10.18653/v1/S19-2023
%P 153-158
Markdown (Informal)
[CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM](https://aclanthology.org/S19-2023) (Mohammadi et al., SemEval 2019)
ACL