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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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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

Defining context-free grammar

Now let's focus on NLU, and to understand it, first we need to understand context-free grammar (CFG) and how it is used in NLU.

Context-free grammar is defined by its four main components. Those four components are shown in this symbolic representation of CFG:

  • A set of non-terminal symbols, N
  • A set of terminal symbols, T
  • A start symbol, S, which is a non-terminal symbol
  • A set of rules called production rules P, for generating sentences

Let's take an example to get better understanding of the context-free grammar terminology:

X ->

Here, X-> is called the phrase structure rule or production rule, P. X ε N means X belongs to non-terminal symbol; ε {N or T} means belongs to either terminal symbols or non-terminal symbols. X can be rewritten in the form of . The rule tells you which element can be rewritten to generate...

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