UTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles
Marcos Aurélio Hermogenes Boriola, Gustavo Henrique Paetzold
Abstract
Offensive language is a common issue on social media platforms nowadays. In an effort to address this issue, the SemEval 2020 event held the OffensEval 2020 shared task where the participants were challenged to develop systems that identify and classify offensive language in tweets. In this paper, we present a system that uses an Ensemble model stacking a BOW model and a CNN model that led us to place 29th in the ranking for English sub-task A.- Anthology ID:
- 2020.semeval-1.297
- 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:
- 2232–2236
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.297
- DOI:
- 10.18653/v1/2020.semeval-1.297
- Cite (ACL):
- Marcos Aurélio Hermogenes Boriola and Gustavo Henrique Paetzold. 2020. UTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2232–2236, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- UTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles (Boriola & Paetzold, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.semeval-1.297.pdf