@inproceedings{rani-ojha-2019-kmi,
title = "{KMI}-Coling at {S}em{E}val-2019 Task 6: Exploring N-grams for Offensive Language detection",
author = "Rani, Priya and
Ojha, Atul Kr.",
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/S19-2119",
doi = "10.18653/v1/S19-2119",
pages = "668--671",
abstract = "In this paper, we present the system description of Offensive language detection tool which is developed by the KMI{\_}Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76{\%}, 87.91{\%} and 44.37{\%} accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.",
}
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%0 Conference Proceedings
%T KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection
%A Rani, Priya
%A Ojha, Atul Kr.
%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 rani-ojha-2019-kmi
%X In this paper, we present the system description of Offensive language detection tool which is developed by the KMI_Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76%, 87.91% and 44.37% accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.
%R 10.18653/v1/S19-2119
%U https://aclanthology.org/S19-2119
%U https://doi.org/10.18653/v1/S19-2119
%P 668-671
Markdown (Informal)
[KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection](https://aclanthology.org/S19-2119) (Rani & Ojha, SemEval 2019)
ACL