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Python Natural Language Processing

You're reading from   Python Natural Language Processing Advanced machine learning and deep learning techniques for natural language processing

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787121423
Length 486 pages
Edition 1st Edition
Languages
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Practical Understanding of a Corpus and Dataset 3. Understanding the Structure of a Sentences 4. Preprocessing 5. Feature Engineering and NLP Algorithms 6. Advanced Feature Engineering and NLP Algorithms 7. Rule-Based System for NLP 8. Machine Learning for NLP Problems 9. Deep Learning for NLU and NLG Problems 10. Advanced Tools 11. How to Improve Your NLP Skills 12. Installation Guide

Some of the facts related to word2vec

Here are some of the facts about the word2vec models that you should keep in mind when you are actually using it:

  • So far, you will have realized that word2vec uses neural networks and this neural network is not a deep neural network. It only has two layers, but it works very well to find out the words similarity.
  • Word2vec neural network uses a simple logistic activation function that does not use non-linear functions.
  • The activation function of the hidden layer is simply linear because it directly passes its weighted sum of inputs to the next layer.

Now, we have seen almost all the major aspects of word2vec, so in the next section, we will look at the application of word2vec.

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