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Hands-On Python Natural Language Processing

You're reading from   Hands-On Python Natural Language Processing Explore tools and techniques to analyze and process text with a view to building real-world NLP applications

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838989590
Length 316 pages
Edition 1st Edition
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Authors (2):
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Mayank Rasu Mayank Rasu
Author Profile Icon Mayank Rasu
Mayank Rasu
Aman Kedia Aman Kedia
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Aman Kedia
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction
2. Understanding the Basics of NLP FREE CHAPTER 3. NLP Using Python 4. Section 2: Natural Language Representation and Mathematics
5. Building Your NLP Vocabulary 6. Transforming Text into Data Structures 7. Word Embeddings and Distance Measurements for Text 8. Exploring Sentence-, Document-, and Character-Level Embeddings 9. Section 3: NLP and Learning
10. Identifying Patterns in Text Using Machine Learning 11. From Human Neurons to Artificial Neurons for Understanding Text 12. Applying Convolutions to Text 13. Capturing Temporal Relationships in Text 14. State of the Art in NLP 15. Other Books You May Enjoy

Summary

In this chapter, we looked at the various steps that are needed to build a natural language vocabulary. These play the most critical role in preprocessing any natural language data. Data preprocessing is probably one of the most important aspects of any machine learning application, and the same applies to NLP as well. When performed properly, these steps help with the machine learning aspects that generally occur after preprocessing the data, consequently providing better results most of the time compared with scenarios where no preprocessing is involved.

In the next chapter, we will use the techniques discussed in this chapter to preprocess data and subsequently build mathematical representations of text that can be understood by machine learning algorithms.

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