@inproceedings{mut-altin-etal-2019-lastus,
title = "{L}a{STUS}/{TALN} at {S}em{E}val-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based {B}i-{LSTM} model",
author = "Mut Altin, Lutfiye Seda and
Bravo Serrano, {\`A}lex and
Saggion, Horacio",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2120",
doi = "10.18653/v1/S19-2120",
pages = "672--677",
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.",
}
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%0 Conference Proceedings
%T LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
%A Mut Altin, Lutfiye Seda
%A Bravo Serrano, Àlex
%A Saggion, Horacio
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F mut-altin-etal-2019-lastus
%X 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.
%R 10.18653/v1/S19-2120
%U https://aclanthology.org/S19-2120
%U https://doi.org/10.18653/v1/S19-2120
%P 672-677
Markdown (Informal)
[LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model](https://aclanthology.org/S19-2120) (Mut Altin et al., SemEval 2019)
ACL