ALT submission for OSACT shared task on offensive language detection

Shammur Chowdhury; Sabit Hassan; Younes Samih; Hamdy Soliman Mubarak Hussien; Ahmed Abdelali; Ammar Rashed

ALT submission for OSACT shared task on offensive language detection

Shammur Chowdhury, Sabit Hassan, Younes Samih, Hamdy Soliman Mubarak Hussien, Ahmed Abdelali, Ammar Rashed

Qatar Computing Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.

Original language	English
Title of host publication	Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools
Pages	61-65
Publication status	Published - May 2020

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Chowdhury, S., Hassan, S., Samih, Y., Hussien, H. S. M., Abdelali, A., & Rashed, A. (2020). ALT submission for OSACT shared task on offensive language detection. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools (pp. 61-65) https://scholar.google.com/citations?view_op=view_citation&hl=en&user=LkSfdoAAAAAJ&pagesize=100&citation_for_view=LkSfdoAAAAAJ:ye4kPcJQO24C

@inproceedings{802e3da407d3481c9847adfc1884fac3,

title = "ALT submission for OSACT shared task on offensive language detection",

abstract = "In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.",

author = "Shammur Chowdhury and Sabit Hassan and Younes Samih and Hussien, {Hamdy Soliman Mubarak} and Ahmed Abdelali and Ammar Rashed",

year = "2020",

month = may,

language = "English",

pages = "61--65",

booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools",

}

Chowdhury, S, Hassan, S, Samih, Y, Hussien, HSM, Abdelali, A & Rashed, A 2020, ALT submission for OSACT shared task on offensive language detection. in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools. pp. 61-65. <https://scholar.google.com/citations?view_op=view_citation&hl=en&user=LkSfdoAAAAAJ&pagesize=100&citation_for_view=LkSfdoAAAAAJ:ye4kPcJQO24C>

TY - GEN

T1 - ALT submission for OSACT shared task on offensive language detection

AU - Chowdhury, Shammur

AU - Hassan, Sabit

AU - Samih, Younes

AU - Hussien, Hamdy Soliman Mubarak

AU - Abdelali, Ahmed

AU - Rashed, Ammar

PY - 2020/5

Y1 - 2020/5

N2 - In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.

AB - In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.

M3 - Conference contribution

SP - 61

EP - 65

BT - Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools

ER -

ALT submission for OSACT shared task on offensive language detection

Abstract

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