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Register a new userOffendES: A New Corpus in Spanish for Offensive Language Research
الإساءة: جثة جديدة باللغة الإسبانية لأبحاث اللغة الهجومية
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أصبح الكشف والتحليلات الهجومية تحليلها مجالا رئيسيا للبحث في معالجة اللغة الطبيعية.تعرض حرية المشاركة في وسائل التواصل الاجتماعي مستخدمين عبر الإنترنت للمشاركات المصممة للتشويه أو إهانة أو تؤذيها وفقا للجنس أو العرق أو الدين أو الإيديولوجية أو الخصائص الشخصية الأخرى.مع التركيز على المصانعين الشباب من المنصات الاجتماعية المعروفة في Twitter، Instagram، و YouTube، قمنا بجمع كوربوس يتكون من 47،128 تعليقات إسبانية يدويا على الفئات المعروفة الهجومية.تعلق مجموعة فرعية من الجثة درجة من الثقة لكل ملصق، لذلك من الممكن أن كل من تصنيف متعدد الطبقات ودراسات الانحدار المتعدد الناتج ممكن.في هذه الورقة، نقدم كوربوس، ومناقشة عملية بناءها، والمستجدات، وبعض التجارب الأولية معها لتكون خطاس أساسي لمجتمع البحث.
Offensive language detection and analysis has become a major area of research in Natural Language Processing. The freedom of participation in social media has exposed online users to posts designed to denigrate, insult or hurt them according to gender, race, religion, ideology, or other personal characteristics. Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of 47,128 Spanish comments manually labeled on offensive pre-defined categories. A subset of the corpus attaches a degree of confidence to each label, so both multi-class classification and multi-output regression studies are possible. In this paper, we introduce the corpus, discuss its building process, novelties, and some preliminary experiments with it to serve as a baseline for the research community.
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Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of
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