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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

Latent Semantic Indexing with Gensim

In Chapter 4, Latent Semantic Indexing with scikit-learn, we learned about the construction of LSI from SVD and used scikit-learn to perform LSI. We also mentioned that the Gensim library has programmed LSI in a few lines of code for production purposes. In this chapter, we will build the LSI model with Gensim. We will also learn how to determine the right number of topics. I’ll also demonstrate to you how to put the model to real use as a search engine. This production-oriented perspective will help data scientists from non-NLP areas to consider stepping into the NLP world.

This chapter covers the following topics:

  • Performing text preprocessing
  • Performing text representation with BoW and TF-IDF
  • Modeling with Gensim
  • Using the coherence score to find the optimal number of topics
  • Understanding the final model
  • Using the model as an information retrieval tool

After completing this chapter, you will be able...

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