论文标题
Exaasc：阿拉伯语的一般基于目标的立场检测语料库
ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language
论文作者
论文摘要
基于目标的立场检测是对目标找到立场的任务。 Twitter是社交媒体中政治讨论的主要来源之一，也是分析对实体立场的最佳资源之一。这项工作提出了一种新的方法，通过使用对源推文中最重要且最有争议的目标的答复的立场来实现基于目标的立场检测。相对于源推文本身检测到该目标，而不限于一组预定义的目标，这是当前最新方法的通常方法。我们提出的新态度导致了一种新的语料库，称为阿拉伯语，这是该领域的低资源语言之一。最后，我们使用BERT评估我们的语料库，并达到了70.69个宏F-SCORE。这表明我们的数据和模型可以在一般目标基态检测系统中起作用。该语料库可公开1。
Target-based Stance Detection is the task of finding a stance toward a target. Twitter is one of the primary sources of political discussions in social media and one of the best resources to analyze Stance toward entities. This work proposes a new method toward Target-based Stance detection by using the stance of replies toward a most important and arguing target in source tweet. This target is detected with respect to the source tweet itself and not limited to a set of pre-defined targets which is the usual approach of the current state-of-the-art methods. Our proposed new attitude resulted in a new corpus called ExaASC for the Arabic Language, one of the low resource languages in this field. In the end, we used BERT to evaluate our corpus and reached a 70.69 Macro F-score. This shows that our data and model can work in a general Target-base Stance Detection system. The corpus is publicly available1.
