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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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Toc

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

PV-DM

The word pairs in PV-DM are arranged as shown in Figure 8.4. It adds the paragraph IDs in the texts then uses a sliding window to form word pairs. For example, Paragraph “1” has “Jupiter overtakes Saturn...”, so the word pairs are (“1”, Saturn), (Jupiter, Saturn), and (overtakes, Saturn).

Figure 8.4 – An overview of Data preparation for PV-DM

Figure 8.4 – An overview of Data preparation for PV-DM

Figure 8.5 shows the neural network for PV-DM.

Figure 8.5 – PV-DM

Figure 8.5 – PV-DM

The word pairs In Figure 8.4 are the inputs for the input and output layers. Different from the word pairs of PV-DBOW in Figure 8.3, the word pairs in Figure 8.4 only have one instance for the paragraph ID. Each paragraph ID is one-hot encoded as a 1 x 500 vector. Again, for example, paragraph “123” shall become a 1 x 500 vector where the 123rd element in the array is 1 and the rest are zeros. All the words are one-hot encoded to be 1 x 10,000 vectors...

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