Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

by Julien Simon, Mona M, Premkumar Rangarajan

Length: 508 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-11-26
ISBN-10: 1801812535
ISBN-13: 9781801812535
Sales Rank: #1531481 (See Top 100 Books)

Work through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI services

Key Features

Get to grips with AWS AI services for NLP and find out how to use them to gain strategic insights
Run Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomes
Understand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2I

Book Description

Natural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.

To start with, you’ll understand the importance of NLP in today’s business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you’ll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You’ll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you’ll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you’ll learn best practices for auto-scaling your NLP inference for enterprise traffic.

Whether you’re new to ML or an experienced practitioner, by the end of this NLP book, you’ll have the confidence to use AWS AI services to build powerful NLP applications.

What you will learn

Automate various NLP workflows on AWS to accelerate business outcomes
Use Amazon Textract for text, tables, and handwriting recognition from images and PDF files
Gain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon Comprehend
Set up end-to-end document processing pipelines to understand the role of humans in the loop
Develop NLP-based intelligent search solutions with just a few lines of code
Create both real-time and batch document processing pipelines using Python

Who this book is for

If you’re an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful.

NLP in the Business Context and Introduction to AWS AI Services
Introducing Amazon Textract
Introducing Amazon Comprehend
Automating Document Processing Workflows
Creating NLP Search
Using NLP to Improve Customer Service Efficiency
Understanding the Voice of Your Customer Analytics
Leveraging NLP to Monetize Your Media Content
Extracting Metadata from Financial Documents
Reducing Localization Costs with Machine Translation
Using Chatbots for Querying Documents
AI and NLP in Healthcare
Improving the Accuracy of Document Processing Workflows
Auditing Named Entity Recognition Workflows
Classifying Documents and Setting up Human in the Loop for Active Learning
Improving the Accuracy of PDF Batch Processing
Visualizing Insights from Handwritten Content
Building Secure, Reliable, and Efficient NLP Solutions

Natural Language Processing with AWS AI Services
Acknowledgments
Foreword
Contributors
About the authors
About the reviewers
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files 
    Download the color images 
    Code in Action
    Conventions used
    Get in touch
    Share Your Thoughts
Section 1:Introduction to AWS AI NLP Services
Chapter 1: NLP in the Business Context and Introduction to AWS AI Services
    Introducing NLP
    Overcoming the challenges in building NLP solutions
    Understanding why NLP is becoming mainstream
    Introducing the AWS ML stack
    Summary
    Further reading
Chapter 2: Introducing Amazon Textract
    Technical requirements
    Setting up your AWS environment
        Signing up for an AWS account
        Creating an Amazon S3 bucket and a folder and uploading objects
        Creating an Amazon SageMaker Jupyter notebook instance
        Changing IAM permissions and trust relationships for the Amazon SageMaker notebook execution role
    Overcoming challenges with document processing
    Understanding how Amazon Textract can help
    Presenting Amazon Textract's product features
        Uploading sample document(s)
        Raw text or text extraction
        Form data and key/value pairs
        Table extraction
        Multiple language support
        Handwriting detection
        Human in the loop
    Using Amazon Textract with your applications
        Textract APIs
        Textract API demo with a Jupyter notebook
        Building applications using Amazon Textract APIs
    Summary
Chapter 3: Introducing Amazon Comprehend
    Technical requirements
    Understanding Amazon Comprehend and Amazon Comprehend Medical
        Challenges associated with setting up ML preprocessing for NLP
        Exploring the benefits of Amazon Comprehend and Comprehend Medical
        Detecting insights in text using Comprehend and Comprehend Medical without preprocessing
        Using these services to gain insights from OCR documents from Amazon Textract
    Exploring Amazon Comprehend and Amazon Comprehend Medical product features
        Discovering Amazon Comprehend
        Deriving diagnoses from a doctor-patient transcript with Comprehend Medical
    Using Amazon Comprehend with your applications
        Architecting applications with Amazon API Gateway, AWS Lambda, and Comprehend
    Summary
Section 2: Using NLP to Accelerate Business Outcomes
Chapter 4: Automating Document Processing Workflows 
    Technical requirements
    Automating document processing workflows
    Setting up compliance and control
        Setting up to solve the use case
        Additional IAM prerequisites
        Automating documents for control and compliance
    Processing real-time document workflows versus batch document workflows
    Summary
    Further reading
Chapter 5: Creating NLP Search
    Technical requirements
    Creating NLP-powered smart search indexes
    Building a search solution for scanned images using Amazon Elasticsearch
        Prerequisites
        Uploading documents to Amazon S3
        Inspecting the AWS Lambda function
        Searching for and discovering data in the Kibana console
    Setting up an enterprise search solution using Amazon Kendra
        In this section, we will cover the steps to get started.
        Walking through the solution
        Searching in Amazon Kendra with enriched filters from Comprehend
    Summary
    Further reading
Chapter 6: Using NLP to Improve Customer Service Efficiency
    Technical requirements
    Introducing the customer service use case
    Building an NLP solution to improve customer service
        Setting up to solve the use case
        Additional IAM prerequisites
        Preprocessing the customer service history data
    Summary
    Further reading
Chapter 7: Understanding the Voice of Your Customer Analytics 
    Technical requirements
    Challenges of setting up a text analytics solution
    Setting up a Yelp review text analytics workflow
        Setting up to solve the use case
        Walking through the solution using Jupyter Notebook
    Summary
    Further reading
Chapter 8: Leveraging NLP to Monetize Your Media Content
    Technical requirements
    Introducing the content monetization use case
    Building the NLP solution for content monetization
        Setting up to solve the use case
        Additional IAM prerequisites
        Uploading the sample video and converting it for broadcast
        Running transcription, finding topics, and creating a VAST ad tag URL
        Inserting ads and testing our video
    Summary
    Further reading
Chapter 9: Extracting Metadata from Financial Documents
    Technical requirements
    Extracting metadata from financial documents
    Setting up the use case
        Setting up the notebook code and S3 Bucket creation
        Analyzing the output of Comprehend Events
    Summary
    Further reading
Chapter 10: Reducing Localization Costs with Machine Translation 
    Technical requirements
    Introducing the localization use case
    Building a multi-language web page using machine translation
        Setting up to solve the use case
        Running the notebook
    Summary
    Further reading
Chapter 11: Using Chatbots for Querying Documents
    Technical requirements
    Introducing the chatbot use case
    Creating an Amazon Kendra index with Amazon S3 as a data source
    Building an Amazon Lex chatbot
    Deploying the solution with AWS CloudFormation
    Summary
    Further reading
Chapter 12: AI and NLP in Healthcare 
    Technical requirements
    Introducing the automated claims processing use case
    Understanding how to extract and validate data from medical intake forms
    Understanding clinical data with Amazon Comprehend Medical
    Understanding invalid medical form processing with notifications
    Understanding how to create a serverless pipeline for medical claims
    Summary
    Further reading
Section 3: Improving NLP Models in Production
Chapter 13: Improving the Accuracy of Document Processing Workflows
    Technical requirements
    The need for setting up HITL processes with document processing
    Seeing the benefits of using Amazon A2I for HITL workflows
    Adding human reviews to your document processing pipelines
        Creating an Amazon S3 bucket
        Creating a private work team in the AWS Console
        Creating a human review workflow in the AWS Console
        Sending the document to Amazon Textract and Amazon A2I by calling the Amazon Textract API
    Summary
    Further reading
Chapter 14: Auditing Named Entity Recognition Workflows
    Technical requirements
    Authenticating loan applications 
    Building the loan authentication solution
        Setting up to solve the use case
        Additional IAM pre-requisites
        Training an Amazon Comprehend custom entity recognizer
        Creating a private team for the human loop
        Extracting sample document contents using Amazon Textract
        Detecting entities using the Amazon Comprehend custom entity recognizer
        Setting up an Amazon A2I human workflow loop
        Reviewing and modifying detected entities
        Retraining Comprehend custom entity recognizer
        Storing decisions for downstream processing
    Summary
    Further reading
Chapter 15: Classifying Documents and Setting up Human in the Loop for Active Learning
    Technical requirements
    Using Comprehend custom classification with human in the loop for active learning
    Building the document classification workflow
        Setting up to solve the use case
        Creating an Amazon Comprehend classification training job
        Creating Amazon Comprehend real-time endpoints and testing a sample document
        Setting up active learning with a Comprehend real-time endpoint using human in the loop
    Summary
    Further reading
Chapter 16: Improving the Accuracy of PDF Batch Processing
    Technical requirements
    Introducing the PDF batch processing use case 
    Building the solution
        Setting up for the solution build
        Additional IAM prerequisites
        Creating a private team for the human loop
        Creating an Amazon S3 bucket
        Extracting the registration document's contents using Amazon Textract
        Setting up an Amazon A2I human workflow loop
        Storing results for downstream processing
    Summary
    Further reading
Chapter 17: Visualizing Insights from Handwritten Content
    Technical requirements
    Extracting text from handwritten images 
        Creating the SageMaker Jupyter notebook 
        Additional IAM prerequisites
        Creating an Amazon S3 bucket
        Extracting text using Amazon Textract
    Visualizing insights using Amazon QuickSight
    Summary
Chapter 18: Building Secure, Reliable, and Efficient NLP Solutions 
    Technical requirements
    Defining best practices for NLP solutions
    Applying best practices for optimization 
        Using an AWS S3 data lake
        Using AWS Glue for data processing and transformation tasks
        Using Amazon SageMaker Ground Truth for annotations
        Using Amazon Comprehend with PDF and Word formats directly
        Enforcing least privilege access
        Obfuscating sensitive data
        Protecting data at rest and in transit
        Using Amazon API Gateway for request throttling
        Setting up auto scaling for Amazon Comprehend endpoints
        Automating monitoring of custom training metrics
        Using Amazon A2I to review predictions
        Using Async APIs for loose coupling
        Using Amazon Textract Response Parser
        Persisting prediction results
        Using AWS Step Function for orchestration
        Using AWS CloudFormation templates
    Summary
    Further reading
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Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend

Key Features

Book Description

What you will learn

Who this book is for

Table of Contents

How to download source code?

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