Abstract
The paper describes systems that our team IRLab_DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B.- Anthology ID:
- 2020.semeval-1.264
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 2006–2011
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.264
- DOI:
- 10.18653/v1/2020.semeval-1.264
- Cite (ACL):
- Apurva Parikh, Abhimanyu Singh Bisht, and Prasenjit Majumder. 2020. IRLab_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2006–2011, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- IRLab_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification (Parikh et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.semeval-1.264.pdf