@inproceedings{ashraf-etal-2022-bfcai,
title = "{BFCAI} at {S}em{E}val-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in {A}rabic Texts",
author = "Ashraf, Nsrin and
Elkazzaz, Fathy and
Taha, Mohamed and
Nayel, Hamada and
Elshishtawy, Tarek",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.123",
doi = "10.18653/v1/2022.semeval-1.123",
pages = "881--884",
abstract = "This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.",
}
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<abstract>This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.</abstract>
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%0 Conference Proceedings
%T BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts
%A Ashraf, Nsrin
%A Elkazzaz, Fathy
%A Taha, Mohamed
%A Nayel, Hamada
%A Elshishtawy, Tarek
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F ashraf-etal-2022-bfcai
%X This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.
%R 10.18653/v1/2022.semeval-1.123
%U https://aclanthology.org/2022.semeval-1.123
%U https://doi.org/10.18653/v1/2022.semeval-1.123
%P 881-884
Markdown (Informal)
[BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts](https://aclanthology.org/2022.semeval-1.123) (Ashraf et al., SemEval 2022)
ACL