Abstract
In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.- Anthology ID:
- 2020.semeval-1.263
- 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:
- 1998–2005
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.263
- DOI:
- 10.18653/v1/2020.semeval-1.263
- Cite (ACL):
- Sandy Kurniawan, Indra Budi, and Muhammad Okky Ibrohim. 2020. IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1998–2005, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation (Kurniawan et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.semeval-1.263.pdf