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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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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

Implementation of simple examples

In this section, we are going to implement the famous word2vec example, which is adding woman and king and subtracting man, and then the resultant vector shows the vector value of queen.

We are not going to train the word2vec model, on our data and then build our own word2vec model because there is a huge amount of data on which Google has already trained their word2vec model and provided us with pre-trained models. Now, if you want to replicate the training process on that much data, then we need a lot of computational resources, so we will use pre-trained word2vec models from Google. You can download the pre-trained model from this link: https://code.google.com/archive/p/Word2vec/.

After clicking on this link, you need to go to the section entitled pre-trained word and phrase vectors, download the model named GoogleNews-vectors-negative300...
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