LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model

Lutfiye Seda Mut Altin, Àlex Bravo Serrano, Horacio Saggion


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S19-2120.pdf
Data
OLID