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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

Automating document processing workflows

We have discussed in the previous chapter how Amazon Textract can help us digitize scanned documents such as PDF and images by extracting text from any document. We also covered how Amazon Comprehend can help us extract insights from these documents, including entities, Personal Identifiable Information (PII), and sentiments.

Now, these services can be used together in an architecture to automate the document processing workflows for most organizations, be it a financial organization or healthcare, which we will cover in Chapter 12, AI and NLP in Healthcare.

Let's start with a fictitious bank, LiveRight Pvt Ltd., whose customers are applying for home loans. We all know this loan origination process involves more than 400 documents to be submitted and reviewed by the bank before approval is forthcoming for your home loan. Automating this process will make it easier for banks as well as customers to get loans. The challenge with automating...

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