@inproceedings{han-etal-2019-jhan014,
title = "jhan014 at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media",
author = "Han, Jiahui and
Wu, Shengtan and
Liu, Xinyu",
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/S19-2116",
doi = "10.18653/v1/S19-2116",
pages = "652--656",
abstract = "In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.",
}
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<abstract>In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.</abstract>
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%0 Conference Proceedings
%T jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media
%A Han, Jiahui
%A Wu, Shengtan
%A Liu, Xinyu
%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 han-etal-2019-jhan014
%X In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.
%R 10.18653/v1/S19-2116
%U https://aclanthology.org/S19-2116
%U https://doi.org/10.18653/v1/S19-2116
%P 652-656
Markdown (Informal)
[jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media](https://aclanthology.org/S19-2116) (Han et al., SemEval 2019)
ACL