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

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803244945

Length 310 pages

Edition 1st Edition

Tools

fastText

Concepts

Mobile Application Development

Author (1):

Chris Kuo

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

Questions

Describe natural language processing (NLP).
What is natural language understanding (NLU)?
What is natural language generation (NLG)?
List some of the NLP modeling techniques used by Gensim.
List some of the most used NLP Python modules.
Once you have answered the previous questions, let’s access https://chat.openai.com/ to search for answers. This time, let’s key in a question, called a “prompt,” to get answers. You are encouraged to experiment with ChatGPT with variations of the questions. For example, you can test the following for Question 1:
1. “Please describe natural language processing.”
2. “Please describe NLP to a high schooler.”
3. “Please describe NLP in one paragraph.”
4. “Please describe NLP with an analogy.”