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

Chapter 1 – Introduction to NLP

Natural language processing is a branch of computer science or artificial intelligence (AI) that uses computer algorithms to analyze, understand, and generate human language data. The algorithms process human language, either in the form of text or voice data, to “understand” its full meaning.
NLU focuses on understanding the meaning of human language. It extracts text or speech input and then analyzes the syntax, semantics, phonology, morphology, and pragmatics in the language.
While NLU is concerned with reading for a computer to comprehend, NLG is about writing for a computer to write. The term generation in NLG refers to an NLP model generating meaningful words or even articles.
The models offered by Gensim include bag-of-words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), latent semantic analysis/indexing (LSA/LSI), Word2Vec, Doc2Vec, Latent Dirichlet Allocation (LDA), and Ensemble LDA.
Gensim...

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