Abstract
we have developed a system based on transfer learning technique depending on universal sentence encoder (USE) embedding that will be trained in our developed model using xgboost classifier to identify the aggressive text data from English content. A reference dataset has been provided from TRAC 2020 to evaluate the developed approach. The developed approach achieved in sub-task EN-A 60.75% F1 (weighted) which ranked fourteenth out of sixteen teams and achieved 85.66% F1 (weighted) in sub-task EN-B which ranked six out of fifteen teams.- Anthology ID:
- 2020.trac-1.16
- Volume:
- Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Ritesh Kumar, Atul Kr. Ojha, Bornini Lahiri, Marcos Zampieri, Shervin Malmasi, Vanessa Murdock, Daniel Kadar
- Venue:
- TRAC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 99–105
- Language:
- English
- URL:
- https://aclanthology.org/2020.trac-1.16
- DOI:
- Cite (ACL):
- Saja Tawalbeh, Mahmoud Hammad, and Mohammad AL-Smadi. 2020. SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pages 99–105, Marseille, France. European Language Resources Association (ELRA).
- Cite (Informal):
- SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost (Tawalbeh et al., TRAC 2020)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2020.trac-1.16.pdf