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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

Understanding word normalization

Most of the time, we don't want to have every individual word fragment that we have ever encountered in our vocabulary. We could want this for several reasons, one being the need to correctly distinguish (for example) the phrase U.N. (with characters separated by a period) from UN (without any periods). We can also bring words to their root form in the dictionary. For instance, am, are, and is can be identified by their root form, be. On another front, we can remove inflections from words to bring them down to the same form. Words car, cars, and car's can all be identified as car.

Also, common words that occur very frequently and do not convey much meaning, such as the articles a, an, and the, can be removed. However, all these highly depend on the use cases. Wh- words, such as when, why, where, and who, do not carry much information in most contexts and are removed as part of a technique called stopword removal, which we will see a little later...

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