@inproceedings{gaanoun-benelallam-2021-sarcasm,
title = "Sarcasm and Sentiment Detection in {A}rabic language A Hybrid Approach Combining Embeddings and Rule-based Features",
author = "Gaanoun, Kamel and
Benelallam, Imade",
editor = "Habash, Nizar and
Bouamor, Houda and
Hajj, Hazem and
Magdy, Walid and
Zaghouani, Wajdi and
Bougares, Fethi and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Touileb, Samia",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wanlp-1.45",
pages = "351--356",
abstract = "This paper presents the ArabicProcessors team{'}s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system{'}s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51{\%} on sarcasm and 71{\%} for sentiment detection.",
}
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<abstract>This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.</abstract>
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%0 Conference Proceedings
%T Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features
%A Gaanoun, Kamel
%A Benelallam, Imade
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F gaanoun-benelallam-2021-sarcasm
%X This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.
%U https://aclanthology.org/2021.wanlp-1.45
%P 351-356
Markdown (Informal)
[Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features](https://aclanthology.org/2021.wanlp-1.45) (Gaanoun & Benelallam, WANLP 2021)
ACL