Abstract
This paper describes 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.
Cite
CITATION STYLE
Altin, L. S. M., Bravo, À., & Saggion, H. (2019). LaSTUS/TALN at SemEval-2019 task 6: Identification and categorization of offensive language in social media with attention-based Bi-LSTM model. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 672–677). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2120
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