Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
Arrow right icon
View More author details
Toc

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

Vectors and why we need them

We're now moving toward the machine learning part of text analysis - this means that we will now start playing a little less with words and a little more with numbers. Even when we used spaCy, the POS-tagging and NER-tagging, for example, was done through statistical models - but the inner workings were largely hidden for us - we passed over Unicode text and after some magic, we have annotated text.

For Gensim however, we're expected to pass vectors as inputs to the IR algorithms (such as LDA or LSI), largely because what's going on under the hood is mathematical operations involving matrices. This means that we have to represent what was previously a string as a vector - and these kind of representations or models are called Vector Space Models [9].

From a mathematical perspective, a vector is a geometric object that has magnitude and...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime