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The Handbook of NLP with Gensim

You're reading from   The Handbook of NLP with Gensim Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803244945
Length 310 pages
Edition 1st Edition
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Author (1):
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Chris Kuo Chris Kuo
Author Profile Icon Chris Kuo
Chris Kuo
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Table of Contents (24) Chapters Close

Preface 1. Part 1: NLP Basics
2. Chapter 1: Introduction to NLP FREE CHAPTER 3. Chapter 2: Text Representation 4. Chapter 3: Text Wrangling and Preprocessing 5. Part 2: Latent Semantic Analysis/Latent Semantic Indexing
6. Chapter 4: Latent Semantic Analysis with scikit-learn 7. Chapter 5: Cosine Similarity 8. Chapter 6: Latent Semantic Indexing with Gensim 9. Part 3: Word2Vec and Doc2Vec
10. Chapter 7: Using Word2Vec 11. Chapter 8: Doc2Vec with Gensim 12. Part 4: Topic Modeling with Latent Dirichlet Allocation
13. Chapter 9: Understanding Discrete Distributions 14. Chapter 10: Latent Dirichlet Allocation 15. Chapter 11: LDA Modeling 16. Chapter 12: LDA Visualization 17. Chapter 13: The Ensemble LDA for Model Stability 18. Part 5: Comparison and Applications
19. Chapter 14: LDA and BERTopic 20. Chapter 15: Real-World Use Cases 21. Assessments 22. Index 23. Other Books You May Enjoy

Bernoulli distributions

The Bernoulli distribution is a simple discrete distribution that describes the probability of binary outcomes in an experiment. There are many examples of Bernoulli distributions in our daily lives. Let’s see some real-world examples:

  • What is the chance of a student passing an exam?
  • What is the chance of a team winning a championship?
  • What is the probability of getting an even number when a fair dice is thrown once?

All these cases have one event or one trial – either “yes” or “no,” or “pass” or “fail.” Let’s see its formal definition.

The formal definition of a Bernoulli distribution

A discrete distribution has two possible outcomes:

P(X = x) = { p if x = 1  q = 1 p if x = 0

This relationship is commonly expressed in an exponential form:

P(x) = p x (1 p) 1x for x (1,0) ...

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