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Natural Language Processing and Computational Linguistics

You're reading from   Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
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Author (1):
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Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Table of Contents (17) Chapters Close

Preface 1. What is Text Analysis? FREE CHAPTER 2. Python Tips for Text Analysis 3. spaCy's Language Models 4. Gensim – Vectorizing Text and Transformations and n-grams 5. POS-Tagging and Its Applications 6. NER-Tagging and Its Applications 7. Dependency Parsing 8. Topic Models 9. Advanced Topic Modeling 10. Clustering and Classifying Text 11. Similarity Queries and Summarization 12. Word2Vec, Doc2Vec, and Gensim 13. Deep Learning for Text 14. Keras and spaCy for Deep Learning 15. Sentiment Analysis and ChatBots 16. Other Books You May Enjoy

Python Tips for Text Analysis

We mentioned in Chapter 1, What is Text Analysis, that we will be using Python throughout the book because it is an easy-to-use and powerful language. In this chapter, we will substantiate these claims, while also providing a revision course in basic Python for text analysis.

Why is this important? While we expect readers of the book to have a background in Python and high-school level math, it is still possible that it's been a while since you've written Python code – and even if you have, the Python code you write during text analysis and string manipulation is quite different from, say, building a website using the web framework Django. Following are the topics we will cover in this chapter:

  • Why Python?
  • Text manipulation in Python
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