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Register a new userOffensive Language Detection in Nepali Social Media
كشف اللغة المسيئة في وسائل التواصل الاجتماعي النيبالي
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غالبا ما تحتوي نصوص وسائل التواصل الاجتماعي مثل منشورات المدونة والتعليقات والتغريدات بلغات هجومية بما في ذلك تعليقات خطاب الكراهية العنصرية والهجمات الشخصية والتحرش الجنسي.لذلك اكتشاف الاستخدام غير المناسب للغة هو أهمية قصوى لسلامة المستخدمين وكذلك لقمع السلوك البغيض والعدوان.الأساليب الحالية لهذه المشكلة متاحة في الغالب لغات غنية بالموارد مثل الإنجليزية والألمانية.في هذه الورقة، نميز اللغة المسيئة في النيبالية، وهي لغة موارد منخفضة، تسليط الضوء على التحديات التي يجب معالجتها لمعالجة نص وسائل الإعلام الاجتماعية النيبالية.نقدم أيضا تجارب للكشف عن اللغة المسيئة باستخدام تعلم الآلات الخاضعة للإشراف.إلى جانب المساهمة في أول مناهج خط الأساس في الكشف عن اللغة الهجومية في النيبالية، نطلق أيضا على مجموعات البيانات المشروح البشرية لتشجيع البحث في المستقبل على هذا الموضوع الحاسم.
Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassment. Detecting inappropriate use of language is, therefore, of utmost importance for the safety of the users as well as for suppressing hateful conduct and aggression. Existing approaches to this problem are mostly available for resource-rich languages such as English and German. In this paper, we characterize the offensive language in Nepali, a low-resource language, highlighting the challenges that need to be addressed for processing Nepali social media text. We also present experiments for detecting offensive language using supervised machine learning. Besides contributing the first baseline approaches of detecting offensive language in Nepali, we also release human annotated data sets to encourage future research on this crucial topic.
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