@inproceedings{mitrovic-etal-2019-nlpup,
title = "nlp{UP} at {S}em{E}val-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection",
author = "Mitrovi{\'c}, Jelena and
Birkeneder, Bastian and
Granitzer, Michael",
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/S19-2127/",
doi = "10.18653/v1/S19-2127",
pages = "722--726",
abstract = "This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40{\%} on the SemEval dataset."
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%0 Conference Proceedings
%T nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
%A Mitrović, Jelena
%A Birkeneder, Bastian
%A Granitzer, Michael
%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 mitrovic-etal-2019-nlpup
%X This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40% on the SemEval dataset.
%R 10.18653/v1/S19-2127
%U https://aclanthology.org/S19-2127/
%U https://doi.org/10.18653/v1/S19-2127
%P 722-726
Markdown (Informal)
[nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection](https://aclanthology.org/S19-2127/) (Mitrović et al., SemEval 2019)
ACL