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
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.123.pdf