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WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
arXiv - CS - Social and Information Networks Pub Date : 2020-09-11 , DOI: arxiv-2009.05456
Yasser Otiefy (WideBot), Ahmed Abdelmalek (WideBot), Islam El Hosary (WideBot)

Communicating through social platforms has become one of the principal means of personal communications and interactions. Unfortunately, healthy communication is often interfered by offensive language that can have damaging effects on the users. A key to fight offensive language on social media is the existence of an automatic offensive language detection system. This paper presents the results and the main findings of SemEval-2020, Task 12 OffensEval Sub-task A Zampieri et al. (2020), on Identifying and categorising Offensive Language in Social Media. The task was based on the Arabic OffensEval dataset Mubarak et al. (2020). In this paper, we describe the system submitted by WideBot AI Lab for the shared task which ranked 10th out of 52 participants with Macro-F1 86.9% on the golden dataset under CodaLab username "yasserotiefy". We experimented with various models and the best model is a linear SVM in which we use a combination of both character and word n-grams. We also introduced a neural network approach that enhanced the predictive ability of our system that includes CNN, highway network, Bi-LSTM, and attention layers.

中文翻译:

WOLI 在 SemEval-2020 任务 12:不同 Twitter 数据集上的阿拉伯语攻击性语言识别

通过社交平台进行交流已成为个人交流和互动的主要方式之一。不幸的是,健康的交流经常受到攻击性语言的干扰,这些语言会对用户产生破坏性影响。在社交媒体上打击攻击性语言的关键是存在自动攻击性语言检测系统。本文介绍了 SemEval-2020、Task 12 OffensEval 子任务 A Zampieri 等人的结果和主要发现。(2020),关于识别和分类社交媒体中的攻击性语言。该任务基于阿拉伯语 OffensEval 数据集 Mubarak 等人。(2020)。在本文中,我们描述了 WideBot AI Lab 为共享任务提交的系统,该系统在 CodaLab 用户名“yasserotiefy”下的黄金数据集上以 Macro-F1 86.9% 的 52 名参与者排名第 10。我们对各种模型进行了试验,最好的模型是线性 SVM,其中我们使用字符和单词 n-gram 的组合。我们还引入了一种神经网络方法,增强了我们系统的预测能力,包括 CNN、高速公路网络、Bi-LSTM 和注意力层。
更新日期:2020-09-21
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