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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

Chapter 5: Creating NLP Search

In the previous chapters, you were introduced to Amazon Textract for extracting text from documents, and Amazon Comprehend to extract insights with no prior Machine Learning (ML) experience as a prerequisite. In the last chapter, we showed you how you can combine these features together to solve a real-world use case for document automation by giving an example of loan processing.

In this chapter, we will use the Amazon Textract and Amazon Comprehend services to show you how you can quickly set up an intelligent search solution with the integration of powerful elements, such as Amazon Elasticsearch, which is a managed service to set up search and log analytics, and Amazon Kendra, which is an intelligent managed search solution powered by ML for natural language search.

We will cover the following topics in this chapter:

  • Going over search use cases and choices for search solutions
  • Building a search solution for scanned images using Amazon...
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