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Register a new userModeling Content and Context with Deep Relational Learning
نمذجة المحتوى والسياق مع التعلم العلائقية العميقة
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يتطلب بناء نماذج لمهام اللغة الطبيعية الواقعية التعامل مع النصوص الطويلة والمحاسبة التبعيات الهيكلية المعقدة.ظهرت تمثيلات رمزية عصبية كوسيلة للجمع بين قدرات التفكير في الأساليب الرمزية، مع تعبير الشبكات العصبية.ومع ذلك، فقد صممت معظم الأطر الموجودة للجمع بين التمثيل العصبي والرمزي لمهام التعلم العلائقية الكلاسيكية التي تعمل على الكون من الكيانات والعلاقات الرمزية.في هذه الورقة، نقدم دراسنا، وهو إطار إعلاني مفتوح المصدر لتحديد النماذج العلائقية العميقة، مصممة لدعم مجموعة متنوعة من سيناريوهات NLP.يدعم إطارنا سهلا التكامل مع تشفير اللغة التعبيرية، ويوفر واجهة لدراسة التفاعلات بين التمثيل والاستدلالية والتعلم.
Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR
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