Deep Learning in Natural Language Processing
author: Jason Weston, Facebook
published: Jan. 19, 2010, recorded: December 2009, views: 29857
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Description
This tutorial will describe recent advances in deep learning techniques for Natural Language Processing (NLP). Traditional NLP approaches favour shallow systems, possibly cascaded, with adequate hand-crafted features. In constrast, we are interested in end-to-end architectures: these systems include several feature layers, with increasing abstraction at each layer. Compared to shallow systems, these feature layers are learnt for the task of interest, and do not require any engineering. We will show how neural networks are naturally well suited for end-to-end learning in NLP tasks. We will study multi-tasking different tasks, new semi-supervised learning techniques adapted to these deep architectures, and review end-to-end structured output learning. Finally, we will highlight how some of these advances can be applied to other fields of research, like computer vision, as well.
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Reviews and comments:
Very informative talk
This tutorial offers a comprehensive exploration of recent strides in leveraging deep learning for Natural Language Processing (NLP). Emphasizing end-to-end architectures over traditional shallow systems, the discussion delves into the merits of learned feature layers, eliminating the need for manual engineering. The tutorial covers multi-tasking, semi-supervised learning techniques, and end-to-end structured output learning, showcasing the versatility of neural networks in NLP. Additionally, it underscores the cross-applicability of these advancements to diverse research domains, extending their impact to fields such as computer vision.
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