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 continued building advanced NLP solutions to address real-world requirements. We focused on asynchronously processing PDF documents and improving their accuracy by reviewing and modifying low - confidence detections using Amazon Textract and Amazon A2I.

We learned how to register companies to the SEC use case with a need to extract text, and then validate and modify specific text lines in the documents before they could be passed to the Partner Integration team for submission to SEC. We considered an architecture built for scale and ease of setup. We assumed that you are the chief architect overseeing this project, and we then proceeded to provide an overview of the solution components in the Introducing the PDF batch processing use case section.

We then went through the prerequisites for the solution build, set up an Amazon SageMaker Notebook instance, cloned our GitHub repository, and started executing the code in the notebook based on the instructions...