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Building A Multi-class XGBoost Model for Arabic Figurative Language

机译：建立阿拉伯比喻语言的多类XGBoost模型

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In the Natural Language Processing (NLP) field, the text classification becomes a task on which many scholars and researchers concentrate. Rhetorical methods in the Arabic language are among the means of linguistic expression that express opinions and feelings through written or spoken texts. It is essential to pay attention to this specialized research point in the Arabic language and in particular in the so-called Arabic rhetoric sciences that are concerned with figurative devices (i.e. simile, hyperbole and sarcasm). In this paper, we build the eXtreme Gradient Boosting (XGBoost) classifier to classify the multi-class Arabic figurative texts. The XGBoost is quite efficient for its speed and performance. The XGBoost classifier was developed, trained, and tested on this Arabic Figurative Corpus (AFC). The performance of the XGBoost classifier obtained as Fl-score is 88%.
机译：在自然语言处理（NLP）领域，文本分类已成为许多学者和研究人员关注的任务。阿拉伯语的修辞方法是通过书面或口头文字表达意见和情感的语言表达方式之一。必须特别注意阿拉伯语的这一专门研究要点，尤其是在涉及比喻装置（即明喻，夸张和讽刺）的所谓阿拉伯修辞学中。在本文中，我们构建了eXtreme Gradient Boosting（XGBoost）分类器，以对多类阿拉伯比喻文本进行分类。 XGBoost的速度和性能非常高效。 XGBoost分类器是在此阿拉伯比喻语料库（AFC）上开发，训练和测试的。以F1得分获得的XGBoost分类器的性能为88％。

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