BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts

Nsrin Ashraf, Fathy Elkazzaz, Mohamed Taha, Hamada Nayel, Tarek Elshishtawy


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.
Anthology ID:
2022.semeval-1.123
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
881–884
Language:
URL:
https://aclanthology.org/2022.semeval-1.123
DOI:
10.18653/v1/2022.semeval-1.123
Bibkey:
Cite (ACL):
Nsrin Ashraf, Fathy Elkazzaz, Mohamed Taha, Hamada Nayel, and Tarek Elshishtawy. 2022. BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 881–884, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts (Ashraf et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.123.pdf