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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

Chapter 14: Auditing Named Entity Recognition Workflows

In the previous chapter, we were introduced to an approach for improving the accuracy of the results we wanted to extract from documents using Amazon Augmented AI (Amazon A2I). We saw that Amazon A2I can be added to a document processing workflow to review model prediction accuracy. This enabled us to include human reviews in LiveRight's check processing system.

In this chapter, we will walk through an extension of the previous approach by including Amazon Comprehend for text-based insights thereby demonstrating an end-to-end process for setting up an auditing workflow for your custom named entity recognition use cases. We put together this solution based on our collective experience and the usage trends we have observed in our careers. We expect to be hands-on throughout the course of this chapter, but we have all the code samples we need to get going.

With machine learning (ML), companies can set up automated document...

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