论文标题
阿拉伯语进攻性语言检测使用机器学习和集成机器学习方法
Arabic Offensive Language Detection Using Machine Learning and Ensemble Machine Learning Approaches
论文作者
论文摘要
这项研究旨在调查应用单个学习者机器学习方法和集合机器学习方法的效果，以进行阿拉伯语的进攻性语言检测。由于文本的书面形式的歧义和非正式性，对阿拉伯社交媒体文本进行分类是一项非常具有挑战性的任务。阿拉伯语具有多种方言，具有不同的词汇和结构，这增加了获得高分类性能的复杂性。我们的研究显示，对单个学习者机器学习方法应用集成机器学习方法的影响很大。在受过训练的合奏机器学习分类器中，Bagging在F1分数中表现出最佳的进攻性语言检测，这超过了最佳单一学习者分类器获得的分数6％。我们的发现凸显了为进攻性语言检测模型促进集合机器学习方法解决方案而投入更多努力的巨大机会。
This study aims at investigating the effect of applying single learner machine learning approach and ensemble machine learning approach for offensive language detection on Arabic language. Classifying Arabic social media text is a very challenging task due to the ambiguity and informality of the written format of the text. Arabic language has multiple dialects with diverse vocabularies and structures, which increase the complexity of obtaining high classification performance. Our study shows significant impact for applying ensemble machine learning approach over the single learner machine learning approach. Among the trained ensemble machine learning classifiers, bagging performs the best in offensive language detection with F1 score of 88%, which exceeds the score obtained by the best single learner classifier by 6%. Our findings highlight the great opportunities of investing more efforts in promoting the ensemble machine learning approach solutions for offensive language detection models.
