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

Summary

In this chapter, we introduced the medical claim processing use case. We then covered how you can use AWS AI services such as Amazon Textract to extract form data from these scanned medical forms. Then, we spoke about how you can perform some postprocessing on the extracted text based on your business rules to validate their form data. Once the form data had been validated, we showed you how to use Amazon Comprehend Medical, as covered in Chapter 3, Introducing Amazon Comprehend, to extract medical insights. Once you have medical insights, this data can be converted into a CSV file and saved in Amazon S3. Once you've done this, you can analyze this data for population health analytics by using Amazon Athena or Amazon QuickSight. We also discussed how to handle invalid claims processing by showing how to quickly configure Amazon SNS through the AWS console and add subscribers. You can notify your subscribers by email regarding the medical claims that have been submitted...