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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

Basic feature of NLP

Apart from the challenges, NLP applications heavily rely on feature that are manually crafted based on various NLP concepts. From this point onwards, we will explore the basic features that are available in the NLP world. Let's dive in!

Parsers and parsing

By parsing sentences, you can derive some of the most important features that can be helpful for almost every NLP application.

We will explore the concept of parser and parsing. Later, we will understand context-free grammar (CFG) and probabilistic context-free grammar (PCFG). We will see how statistical parsers are developed. If you want to make your own parser, then we will explain the procedure to do so, or if you want to tweak the existing parser...

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