A Great Tutorial for NLP using Deep Learning

There is a tutorial given at Stanford University on NLP (Natural Language Processing) and how to make use of deep learning for NLP.

You can find the video tutorial here: Part 1 and Part 2.

From this tutorial, I learned that the difficulty of NLP is to transform the word (in text) into a vector (a tuple of length N with numerical values) which preserve the property of the word (e.g. its synonyms) in high dimensional space. For instance, the distance between the vector of the word "Happy" and the vector of the word "Joyful" should be very closed in the subspace that is defined by the representation. Learning a good representation of the words or sequence of words (e.g. sentences) is very important because it has a direct effect to the performance of the NLP task you have in hand. This is where deep learning might fill in the gap. Instead of handcrafting the representation of the words, it goes a step further to learn the representation of the words using deep learning techniques presented in the tutorial. You can see the results of many published papers about the performance improvements of using deep learning for NLP.