论文标题
Woli在Semeval-2020任务12：不同Twitter数据集上的阿拉伯进攻语言标识
WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
论文作者
论文摘要
通过社交平台进行沟通已成为个人沟通和互动的主要手段之一。不幸的是，健康沟通通常会受到进攻性语言的干扰，这可能会对用户产生破坏性影响。在社交媒体上打击进攻语言的关键是存在自动进攻性语言检测系统。本文介绍了Semeval-2020的结果和主要发现，任务12攻击子任务A Zampieri等。 （2020），关于在社交媒体中识别和分类进攻语言。该任务基于阿拉伯犯罪数据集Mubarak等。 （2020）。在本文中，我们描述了Widebot AI实验室提交的共享任务的系统，该任务在52名参与者中排名第10，而Macro-F1的52个参与者在Codalab用户名中的Golden DataSet中排名为86.9％。我们尝试了各种模型，最佳模型是线性SVM，其中我们使用字符和单词n-grams的组合。我们还引入了一种神经网络方法，从而增强了系统的预测能力，包括CNN，公路网络，BI-LSTM和注意力层。
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.
