Abstract
In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words’ representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.- Anthology ID:
- S19-2117
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 657–661
- Language:
- URL:
- https://aclanthology.org/S19-2117
- DOI:
- 10.18653/v1/S19-2117
- Cite (ACL):
- Johnny Torres and Carmen Vaca. 2019. JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 657–661, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks (Torres & Vaca, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S19-2117.pdf