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

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801812535

Length 508 pages

Edition 1st Edition

Languages

Processing

Tools

Amazon Textract

Concepts

Mobile Application Development

Authors (2):

Mona M

Premkumar Rangarajan

View More author details

Table of Contents (23) Chapters

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

Deploying the solution with AWS CloudFormation

In the preceding two sections, we saw a step-by-step account of how to create an index and associate documents from an Amazon S3 bucket as a data source using Amazon Kendra, and how to build an Amazon Lex chatbot and integrate that with our Amazon Kendra index using a built-in intent. This completes the majority of our solution build. In this section, we will use an AWS CloudFormation template sample from the accompanying repository for the blog post we mentioned in the previous section: https://github.com/aws-samples/aws-lex-web-ui/tree/master/templates.

Let's get started:

Click https://github.com/aws-samples/aws-lex-web-ui/tree/master/templates and scroll down to the Launch section on the web page and click the first Launch Stack button for the CodeBuild mode.
Figure 11.13 – Launching AWS CloudFormation stack
This action will open the AWS CloudFormation service in your AWS Management Console and take you to...

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

Table of Contents (23) Chapters

Deploying the solution with AWS CloudFormation

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Authors (2)