论文标题
Urdufake@fire2021：在乌尔都语中的假新闻标识的共享曲目
UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu
论文作者
论文摘要
这项研究报告了第二个名为Urdufake@Fire2021的共享任务，以识别乌尔都语的假新闻检测。这是一个二进制分类问题，其中任务是将给定的新闻文章分为两个类别：（i）真实新闻，或（ii）假新闻。在这项共同的任务中，来自7个不同国家（中国，埃及，以色列，印度，墨西哥，巴基斯坦和阿联酋）的34个团队注册参加了共同的任务，18个团队提交了他们的实验结果，并提交了11个团队。所提出的系统基于各种基于计数的功能，并使用了不同的分类器以及神经网络体系结构。随机梯度下降（SGD）算法的表现优于其他分类器，并达到0.679 F-SCORE。
This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.
