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Natural Language Processing with AWS AI Services

You're reading from   Natural Language Processing with AWS AI Services Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801812535
Length 508 pages
Edition 1st Edition
Languages
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Authors (2):
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Mona M Mona M
Author Profile Icon Mona M
Mona M
Premkumar Rangarajan Premkumar Rangarajan
Author Profile Icon Premkumar Rangarajan
Premkumar Rangarajan
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Toc

Table of Contents (23) Chapters Close

Preface 1. Section 1:Introduction to AWS AI NLP Services
2. Chapter 1: NLP in the Business Context and Introduction to AWS AI Services FREE CHAPTER 3. Chapter 2: Introducing Amazon Textract 4. Chapter 3: Introducing Amazon Comprehend 5. Section 2: Using NLP to Accelerate Business Outcomes
6. Chapter 4: Automating Document Processing Workflows 7. Chapter 5: Creating NLP Search 8. Chapter 6: Using NLP to Improve Customer Service Efficiency 9. Chapter 7: Understanding the Voice of Your Customer Analytics 10. Chapter 8: Leveraging NLP to Monetize Your Media Content 11. Chapter 9: Extracting Metadata from Financial Documents 12. Chapter 10: Reducing Localization Costs with Machine Translation 13. Chapter 11: Using Chatbots for Querying Documents 14. Chapter 12: AI and NLP in Healthcare 15. Section 3: Improving NLP Models in Production
16. Chapter 13: Improving the Accuracy of Document Processing Workflows 17. Chapter 14: Auditing Named Entity Recognition Workflows 18. Chapter 15: Classifying Documents and Setting up Human in the Loop for Active Learning 19. Chapter 16: Improving the Accuracy of PDF Batch Processing 20. Chapter 17: Visualizing Insights from Handwritten Content 21. Chapter 18: Building Secure, Reliable, and Efficient NLP Solutions 22. Other Books You May Enjoy

Section 3: Improving NLP Models in Production

In this section, we will dive deep into some strategies for how to include a human in the loop for different purposes in a document processing workflow and cover best practices on how to autoscale your NLP inference for enterprise traffic.

This section comprises the following chapters:

  • Chapter 13, Improving the Accuracy of Document Processing Workflows
  • Chapter 14, Auditing Named Entity Recognition Workflows
  • Chapter 15, Classifying Documents and Setting up Human in the Loop for Active Learning
  • Chapter 16, Improving the Accuracy of PDF Batch Processing
  • Chapter 17, Visualizing Insights from Handwritten Content
  • Chapter 18, Building Secure, Reliable, and Efficient NLP Solutions
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