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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

Summary

In this chapter, we have seen all the details related to the rule-based system and how the rule-based approach helps us to develop rapid prototypes for complex problems with high accuracy. We have seen the architecture of the rule-based system. We have learned about the advantages, disadvantages, and challenges for the rule-based system. We have seen how this system is helpful to us for developing NLP applications such as grammar correction systems, chatbots, and so on. We have also discussed the recent trends for the rule-based system.

In the next chapter, we will learn the other main approaches called machine learning, to solve NLP applications. The upcoming chapter will give you all the details about which machine learning algorithms you need to use for developing NLP applications. We will see supervised ML, semi-supervised ML, and unsupervised ML techniques. We will...

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