Book Image

Natural Language Processing with AWS AI Services

By : Mona M, Premkumar Rangarajan
Book Image

Natural Language Processing with AWS AI Services

By: Mona M, Premkumar Rangarajan

Overview of this book

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.
Table of Contents (23 chapters)
1
Section 1:Introduction to AWS AI NLP Services
5
Section 2: Using NLP to Accelerate Business Outcomes
15
Section 3: Improving NLP Models in Production

Understanding how to extract and validate data from medical intake forms

In this section, we will show you how to use Amazon Textract to extract key-value pairs or form data from a medical intake form. Then, using simple logic, you will verify whether the extracted values are valid or invalid.

If you have not done so in the previous chapters, you will have to create an Amazon SageMaker Jupyter notebook and set up Identity and Access Management (IAM) permissions for that notebook role. By doing so, you will be able to access the AWS services we will use in this notebook. After that, you will need to clone this book's GitHub repository (https://github.com/PacktPublishing/Natural-Language-Processing-with-AWS-AI-Services), go to the Chapter 12 folder, and open the ch 12 automating claims processing.ipynb notebook.

Note:

Make sure that the IAM role in the notebook has AmazonSNSFullAccess, AmazonComprehendMedicalFullAccess, and AmazonTextractFullAccess.

Now, using this notebook...