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Natural Language Processing and Computational Linguistics
Natural Language Processing and Computational Linguistics

Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Profile Icon Bhargav Srinivasa-Desikan
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (7 Ratings)
Paperback Jun 2018 306 pages 1st Edition
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Arrow left icon
Profile Icon Bhargav Srinivasa-Desikan
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$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (7 Ratings)
Paperback Jun 2018 306 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$43.99
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Renews at $19.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Natural Language Processing and Computational Linguistics

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

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...

Text manipulation in Python

We mentioned earlier in the chapter that the way we represent text in Python is through strings. So how do we specify that an object is a string?

word = "Bonjour World!"

Now the word variable contains the text, Bonjour World!. Note how we used double quotes around the text that we intend to use - while single quotes also work; if we also wish to use a single quote in our string, we would need to use double quotes. Printing our word is straightforward, where all we need to do is use the print function. Remember to use parentheses if we are coding in Python 3!

print(word)
Bonjour World!

We don't have to use variables to be able to print string though - we can also just do:

print("Bonjour World!")
Bonjour World!

Be careful not to enclose your variable in quotations though! Consider this example:

print("word")
word

This...

Summary

With the knowledge of the functions and strategies we have discussed, our text analysis can be aided; it is often when we are doing large scale text analysis that a small error can lead to completely nonsense results (remember garbage in, garbage out from Chapter 1, What is Text Analysis?).

We finish this mini-chapter with a few useful links on basic text manipulation:

  1. Printing and Manipulating Text [9]: Basic manipulation and printing of text, recommended if interested in how to display text in different ways.
  2. Manipulating Strings [10]: Basic String functions as well as exercises, useful for the further practice of string manipulation.
  3. Manipulating Strings in Python [11]: Similar to the two-preceding links includes a section on escape sequences as well.
  4. Text Processing in Python (book) [12]: Unlike the other links, this is a whole book. It covers the very fundamentals...
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Key benefits

  • • Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras
  • • Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms
  • • Learn deep learning techniques for text analysis

Description

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

Who is this book for?

This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!

What you will learn

  • • Why text analysis is important in our modern age
  • • Understand NLP terminology and get to know the Python tools and datasets
  • • Learn how to pre-process and clean textual data
  • • Convert textual data into vector space representations
  • • Using spaCy to process text
  • • Train your own NLP models for computational linguistics
  • • Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn
  • • Employ deep learning techniques for text analysis using Keras

Product Details

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Publication date : Jun 29, 2018
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781788838535
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Product Details

Publication date : Jun 29, 2018
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781788838535
Category :
Languages :
Tools :

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Table of Contents

16 Chapters
What is Text Analysis? Chevron down icon Chevron up icon
Python Tips for Text Analysis Chevron down icon Chevron up icon
spaCy's Language Models Chevron down icon Chevron up icon
Gensim – Vectorizing Text and Transformations and n-grams Chevron down icon Chevron up icon
POS-Tagging and Its Applications Chevron down icon Chevron up icon
NER-Tagging and Its Applications Chevron down icon Chevron up icon
Dependency Parsing Chevron down icon Chevron up icon
Topic Models Chevron down icon Chevron up icon
Advanced Topic Modeling Chevron down icon Chevron up icon
Clustering and Classifying Text Chevron down icon Chevron up icon
Similarity Queries and Summarization Chevron down icon Chevron up icon
Word2Vec, Doc2Vec, and Gensim Chevron down icon Chevron up icon
Deep Learning for Text Chevron down icon Chevron up icon
Keras and spaCy for Deep Learning Chevron down icon Chevron up icon
Sentiment Analysis and ChatBots Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6
(7 Ratings)
5 star 42.9%
4 star 14.3%
3 star 14.3%
2 star 14.3%
1 star 14.3%
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David Baron Aug 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
15 chapters of fast-paced learning - the author gives you the bootstrapped resources for success with NLP. Every concept is explained clearly and with powerful examples and analogies. Increasingly complex concepts are stacked upon each other to produce a comprehensive overview of everything in NLP, from linguistics-based methods to bag-of-words approaches, from k-means clustering and LDA models to the inevitable methods of deep learning. Highly recommended.
Amazon Verified review Amazon
TM Raghavan Aug 17, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
What I liked about this book is its style of delivery, an easy read one. As it states in the beginning, though fluency in Python or NLP concepts is better to make full use of the ideas, it still contains lots of code examples and analysis of their results, like any good book on this genre. The breadth and and the depth from the coverage of the book's focus are sufficient in my opinion.What I find missing in the book is a section on Glossary, encompassing statistics, vector /matrix algebra, NLP/IR, ANN (CNN/RNN), that are relevant to the readers' requirements. Perhaps this can be given in the beginning itself (like a Forward).Overall I find the book very useful for the students of final year Under-Graduate and Graduate programs besides researchers whose foci are outside of basic NLP but would want to analyse large texts for their research.
Amazon Verified review Amazon
Juan Oct 08, 2018
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Great book, up to date, engaging and covers a lot of topics clearly.
Amazon Verified review Amazon
Victor Aug 27, 2018
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Although the introduction states that is necessary be fluent in Python, this book is feasible for anyone who has just a basic understanding of Python or any other programming language. The book describes profoundly all the steps of a NLP analysis, setting comprehensible examples that allows the reader to understand every aspect of NLP. I would consider this book as an advanced and complete introduction, recommended to anyone who wants to begin and follow further on the topic but getting a solid base from this book. I would recommend it.
Amazon Verified review Amazon
Itai Aug 14, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Writing is a profession. Even excellent engineers may be bad writers, this is exactly what happens here.Super long sentences on trivial topics, and then just a taste from the really important stuff.For example: having several full pages on 1st grade stuff as "lower(), upper(), isNumeric()", having several pages on word2Vec King-man+woman = queen, and then have only a few lines about Doc2Vec which is the most important thing.
Amazon Verified review Amazon
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