IRLab_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification

Apurva Parikh, Abhimanyu Singh Bisht, Prasenjit Majumder


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)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.264.pdf