Deep Learning in Natural Language Processing

author: Ronan Collobert, NEC Laboratories America, Inc.
author: Jason Weston, NEC Laboratories America, Inc.
published: Jan. 19, 2010,   recorded: December 2009,   views: 3523
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 57:39
!NOW PLAYING
Watch Part 2
Part 2 48:11
!NOW PLAYING

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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Morteza Shahriari Nia, July 3, 2014 at 10:56 p.m.:

Very informative talk

Write your own review or comment:

make sure you have javascript enabled or clear this field: