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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

Machine Learning for NLP Problems

We have seen the basic and the advanced levels of feature engineering. We have also seen how rule-based systems can be used to develop NLP applications. In this chapter, we will develop NLP applications, and to develop the applications, we will use machine learning (ML) algorithms. We will begin with the basics of ML. After this, we will see the basic development steps of NLP applications that use ML. We will mostly see how to use ML algorithms in the NLP domain. Then, we will move towards the features selection section. We will also take a look at hybrid models and post-processing techniques.

This is the outline of this chapter given as follows:

  • Understanding the basics of machine learning
  • Development steps for NLP application
  • Understanding ML algorithms and other concepts
  • Hybrid approaches for NLP applications

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