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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

Why Python?

In Python, we re text in the form of string [1], which are objects of the str [2] class. They are an immutable sequence of Unicode code points or characters. It is important to make a careful distinction here, though; in Python 3, all strings are by default Unicode, but in Python 2, the str class is limited to ASCII code, and there is a Unicode class to deal with Unicodes.

Unicode is merely an encoding language or a way we handle text. For example, the Unicode value for the letter Z is U+005A. There are many encoding types, and historically in Python, developers were expected to deal with different encodings on their own, with all the low-level action happening in bytes. In fact, the shift in the way Python handles Unicode has led to a lot of discussions [3], criticism [4], and praise [5] within the community. It also remains an important point of contention when we...

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