@inproceedings{elgabry-etal-2021-contextual,
title = "A Contextual Word Embedding for {A}rabic Sarcasm Detection with Random Forests",
author = "Elgabry, Hazem and
Attia, Shimaa and
Abdel-Rahman, Ahmed and
Abdel-Ate, Ahmed and
Girgis, Sandra",
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-1.43",
pages = "340--344",
abstract = "Sarcasm detection is of great importance in understanding people{'}s true sentiments and opinions. Many online feedbacks, reviews, social media comments, etc. are sarcastic. Several researches have already been done in this field, but most researchers studied the English sarcasm analysis compared to the researches are done in Arabic sarcasm analysis because of the Arabic language challenges. In this paper, we propose a new approach for improving Arabic sarcasm detection. Our approach is using data augmentation, contextual word embedding and random forests model to get the best results. Our accuracy in the shared task on sarcasm and sentiment detection in Arabic was 0.5189 for F1-sarcastic as the official metric using the shared dataset ArSarcasmV2 (Abu Farha, et al., 2021).",
}
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%0 Conference Proceedings
%T A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests
%A Elgabry, Hazem
%A Attia, Shimaa
%A Abdel-Rahman, Ahmed
%A Abdel-Ate, Ahmed
%A Girgis, Sandra
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F elgabry-etal-2021-contextual
%X Sarcasm detection is of great importance in understanding people’s true sentiments and opinions. Many online feedbacks, reviews, social media comments, etc. are sarcastic. Several researches have already been done in this field, but most researchers studied the English sarcasm analysis compared to the researches are done in Arabic sarcasm analysis because of the Arabic language challenges. In this paper, we propose a new approach for improving Arabic sarcasm detection. Our approach is using data augmentation, contextual word embedding and random forests model to get the best results. Our accuracy in the shared task on sarcasm and sentiment detection in Arabic was 0.5189 for F1-sarcastic as the official metric using the shared dataset ArSarcasmV2 (Abu Farha, et al., 2021).
%U https://aclanthology.org/2021.wanlp-1.43
%P 340-344
Markdown (Informal)
[A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests](https://aclanthology.org/2021.wanlp-1.43) (Elgabry et al., WANLP 2021)
ACL