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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

Advanced Feature Engineering and NLP Algorithms

In this chapter, we will look at an amazing and simple concept called word to vector (word2vec). This concept was developed by a team of researchers led by Tomas Mikolov at Google. As we all know, Google provides us with a lot of great products and concepts. Word2vec is one of them. In NLP, developing tools or techniques that can deal with the semantics of words, phrases, sentences, and so on are quite a big deal, and the word2vec model does a great job of figuring out the semantics of words, phrases, sentences, paragraphs, and documents. We are going to jump into this vectorization world and live our life in it for a while. Don't you think this is quite amazing? We will be starting from the concepts and we will end with some fun and practical examples. So, let's begin.

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