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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

Summary

We reviewed a few selected real-world use cases in this chapter to demonstrate the breadth and depth of the techniques that we have learned about in this book. We trust this chapter has inspired you with new ideas, motivated you to invent new applications, and showed you how to apply the code examples that we have included in this book.

NLP keeps evolving at an unprecedented speed. ChatGPT, CPT-4, Llama 2.0, and so on were all developed in 2023. It is foreseeable that more and more generative AI models will emerge. With the knowledge in this book, you will be able to transition to generative NLP. This book helped you familiarize yourself with the fundamentals of NLP, including concepts such as tokenization, part-of-speech tagging, named entity recognition, syntactic parsing, LSA, LDA, and BERTopic. These techniques form the basis for your journey into generative NLP. Generative NLP heavily relies on neural networks. This book also presented the basics of neural network architectures...

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