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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Copy the created bucket name, open Chapter 05/Ch05-Kendra Search.ipynb, and paste it in the following cell in place of '<your s3 bucket name>' to get started."

A block of code is set as follows:

# Define IAM role
role = get_execution_role()
print("RoleArn: {}".format(role))
sess = sagemaker.Session()
s3BucketName = '<your s3 bucket name>'
prefix = 'chapter5'

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    <body>
        <h1>Family Bank Holdings</h1>
        <h3>Date: <span id="date"></span></h3>
        <div id="home">
          <div id="hometext">
        <h2>Who we are and what we do</h2>

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "You will see that the page has a few headings and then a paragraph talking about Family Bank, a subsidiary of LiveRight Holdings."

Tips or Important Notes

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