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Register a new userA Semi-Supervised Approach to Detect Toxic Comments
نهج شبه مشغل للكشف عن التعليقات السامة
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تحتوي التعليقات السامة على أشكال لغة غير مقبولة مستهدفة نحو مجموعات أو أفراد.تصبح هذه الأنواع من التعليقات مصدر قلق خطير للمنظمات الحكومية والمجتمعات عبر الإنترنت ومنصات وسائل التواصل الاجتماعي.على الرغم من وجود بعض الأساليب للتعامل مع اللغة غير المقبولة، فإن معظمها يركز على التعلم الإشراف واللغة الإنجليزية.في هذه الورقة، نتعامل مع اكتشاف التعليق السام كاستراتيجية شبه مشتركة على رسم بياني غير متجانس.نقوم بتقييم النهج على مجموعة بيانات سامة من اللغة البرتغالية، مما يتفوق على العديد من الأساليب القائمة على الرسم البياني وتحقيق نتائج تنافسية مقارنة بمناطق المحولات.
Toxic comments contain forms of non-acceptable language targeted towards groups or individuals. These types of comments become a serious concern for government organizations, online communities, and social media platforms. Although there are some approaches to handle non-acceptable language, most of them focus on supervised learning and the English language. In this paper, we deal with toxic comment detection as a semi-supervised strategy over a heterogeneous graph. We evaluate the approach on a toxic dataset of the Portuguese language, outperforming several graph-based methods and achieving competitive results compared to transformer architectures.
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In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task's main aim was to identify spans to which a given text's toxicity could be attributed. The task is challenging mainly due to two constraints:
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