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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

Challenges for the rule-based system

Let's look at some of the challenges in the RB approach:

  • It is not easy to mimic the behavior of a human.
  • Selecting or designing architecture is the critical part of the RB system.
  • In order to develop the RB system, you need to be an expert of the specific domain which generates rules for us. For NLP we need linguists who know how to analyze language.
  • Natural language is itself a challenging domain because it has so many exception cases and covering those exceptions using rules is also a challenging task, especially when you have a large amount of rules.
  • Arabic, Gujarati, Hindi, and Urdu are difficult to implement in the RB system because finding a domain expert for these languages is a difficult task. There are also less tools available for the described languages to implement the rules.
  • Time consumption of human effort is too high.
  • ...
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