Abstract
In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.- Anthology ID:
- S19-2116
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 652–656
- Language:
- URL:
- https://aclanthology.org/S19-2116
- DOI:
- 10.18653/v1/S19-2116
- Cite (ACL):
- Jiahui Han, Shengtan Wu, and Xinyu Liu. 2019. jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 652–656, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (Han et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S19-2116.pdf