Abstract
We present a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the SemEval 2019 Task 6 which comprises three different sub-tasks, namely A: Offensive Language Detection, B: Categorization of Offensive Language, C: Offensive Language Target Identification. We used a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags. Our approach achieves good performance in the three sub-tasks.- Anthology ID:
- S19-2120
- 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:
- 672–677
- Language:
- URL:
- https://aclanthology.org/S19-2120
- DOI:
- 10.18653/v1/S19-2120
- Cite (ACL):
- Lutfiye Seda Mut Altin, Àlex Bravo Serrano, and Horacio Saggion. 2019. LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 672–677, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model (Mut Altin et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S19-2120.pdf
- Data
- OLID