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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

Handling corpus-raw sentences

In the previous section, we were processing on raw text and looked at concepts at the sentence level. In this section, we are going to look at the concepts of tokenization, lemmatization, and so on at the word level.

Word tokenization

Word tokenization is defined as the process of chopping a stream of text up into words, phrases, and meaningful strings. This process is called word tokenization. The output of the process are words that we will get as an output after tokenization. This is called a token.

Let's see the code snippet given in Figure 4.11 of tokenized words:

Figure 4.11: Word tokenized code snippet

The output of the code given in Figure 4.11 is as follows:

The input for word tokenization...

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