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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

Development steps for NLP applications

In this section, we will discuss the steps of developing NLP applications using ML algorithms. These steps vary from domain to domain. For NLP applications, the visualization of data does not play that much of a critical role, whereas the visualization of data for an analytical application will give you a lot of insight. So, it will change from application to application and domain to domain. Here, my focus is the NLP domain and NLP applications, and when we look at the code, I will definitely recall the steps that I'm describing here so that you can connect the dots.

I have divided the development steps into two versions. The first version is taking into account that it's the first iteration for your NLP application development. The second version will help you with the possible steps that you can consider after your first iteration...

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