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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
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Section 2: Using NLP to Accelerate Business Outcomes
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15
Section 3: Improving NLP Models in Production

Section 2: Using NLP to Accelerate Business Outcomes

In this section, we dive deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, design and development approaches, and a step-by-step guide on how to set up and run these examples, along with access to a GitHub repository. We will also dive deep into the use case of how AWS NLP solutions can help achieve operational efficiency in healthcare with an automated claims adjunction use case.

This section comprises the following chapters:

  • Chapter 4, Automating Document Processing Workflows
  • Chapter 5, Creating NLP Search
  • Chapter 6, Using NLP to Improve Customer Service Efficiency
  • Chapter 7, Understanding the Voice of Your Customer Analytics
  • Chapter 8, Leveraging NLP to Monetize Your Media Content
  • Chapter 9, Extracting Metadata from Financial Documents
  • Chapter...

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