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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

The real-world applications of Doc2Vec

The Doc2Vec technique has been used in many fields in which text data is the most important asset. Let me give some examples of the applications.

Job boards and professional social networks use recommender systems to recommend similar job postings. When you look for a job on LinkedIn or Indeed.com, you may see similar job postings presented next to your target job posting. It is done by Doc2Vec. Doc2Vec also is used by companies such as Airbnb and Alibaba to build their product recommendation systems [3] [4].

Legal professionals need a legal document recommendation system that can automatically pull similar judgments to prepare their arguments in the court. Legal textual information is domain specific. Dhanani, Mehta, and Rana presented that Doc2Vec can perform very rich embedding results [5] [6]. Doc2Vec was even leveraged to uncover the relationships between diseases and disease-genes associations. Gligorijevic et al. [7] found the embedding...

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