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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

Rule-Based System for NLP

We learned to derive various features by using the concepts of linguistics and statistics in Chapter 5, Feature Engineering and NLP Algorithms and Chapter 6, Advanced Feature Engineering and NLP Algorithms. For developing an NLP application, these features are going to be fed into the algorithms. These algorithms take features as input. As you know, we are referring to algorithms as black boxes that perform some kind of magic and gives us the appropriate output. Refer to Figure 7.1, which demonstrates our journey so far:

Figure 7.1: Stages we have learned so far

Congratulations, you have learned a lot about NLP, and specifically about the NLU!

Now, it is high time for us to explore the algorithms which we use to develop NLP applications. We refer to these algorithms, techniques, or approaches as our black boxes and their logic is works as some magic...

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