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Register a new userSemi-Supervised Joint Estimation of Word and Document Readability
تقدير مشترك شبه مشار إليه للكلمة والوثيقة
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سرية أو صعوبة تقدير الكلمات والمستندات قد تم التحقيق بشكل مستقل في الأدب، غالبا ما يفترض وجود موارد مشروحة مكثفة للآخر.بدافع تحليلنا الذي يظهر أن هناك علاقة متكررة بين Word ووثيقة صعوبة، نقترح بشكل مشترك بين الكلمة والوثائق صعوبة من خلال شبكة اتصال بيانية (GCN) في أزياء شبه مشهورة.تكشف نتائجنا التجريبية أن الأسلوب القائم على GCN يمكن أن يحقق دقة أعلى من خطوط الأساس القوية، ويبقى قويا حتى مع كمية أصغر من البيانات المسمى.
Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other. Motivated by our analysis showing that there is a recursive relationship between word and document difficulty, we propose to jointly estimate word and document difficulty through a graph convolutional network (GCN) in a semi-supervised fashion. Our experimental results reveal that the GCN-based method can achieve higher accuracy than strong baselines, and stays robust even with a smaller amount of labeled data.
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