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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? 2. Python Tips for Text Analysis FREE CHAPTER 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

Vector transformations in Gensim

Now that we know what vector transformations are, let's get used to creating them, and using them. We will be performing these transformations with Gensim, but even scikit-learn can be used. We'll also have a look at scikit-learn's approach later on.

Let's create our corpus now. We discussed earlier that a corpus is a collection of documents. In our examples, each document would just be one sentence, but this is obviously not the case in most real-world examples we will be dealing with. We should also note that once we are done with preprocessing, we get rid of all punctuation marks - as for as our vector representation is concerned, each document is just one sentence.

Of course, before we start, be sure to install Gensim. Like spaCy, pip or conda is the best way to do this based on your working environment.

from gensim import...
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