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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

By : M, Premkumar Rangarajan
5 (21)
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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

5 (21)
By: 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)
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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

Introducing the automated claims processing use case

In the healthcare industry, there were approximately 6.1 billion medical claims submitted in 2018 according to the 2018 CAHQ index report (https://www.caqh.org/sites/default/files/explorations/index/report/2018-index-report.pdf), and this number is expected to continue rising in the upcoming years.

Healthcare payer companies are constantly looking for efficient and cost-effective ways to process such volumes of claims in a scalable manner. With the current manual process of claim processing, it takes too much time to process these claims. So, healthcare companies are looking at AI and ML approaches to automating and digitizing these claims. Once they can digitize these, it becomes really easy to drive insights such as improving the population's overall health. Moreover, analyzing these claim documents might help you identify behaviors that can help prevent a medical condition from being developed. Also, healthcare...

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