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

Setting up an enterprise search solution using Amazon Kendra

In this section, we will cover how you can quickly create an end-to-end serverless document search application using Amazon Kendra.

In this section, we will cover the steps to get started.

Git cloning the notebook

We will walk through the steps to git clone the notebook and show code samples to set up the kendra based search architecture using simple boto3 APIs.

In the SageMaker Jupyter notebook you set up in the previous chapters, Git clone https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services/.
Go to Chapter 05/Ch05-Kendra Search.ipynb and start running the notebook.
Note:
Please add Kendra IAM access to the SageMaker notebook IAM role so that you can call Kendra APIs through this notebook. In previous chapters, you already added IAM access to Amazon Comprehend and Textract APIs from the SageMaker notebook.

Creating an Amazon S3 bucket

We will show you how you can...

The rest of the chapter is locked