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 learned why metadata extraction is really important before looking at the use case for LiveRight, our fictitious bank, which had acquisitions that made a press release statement. Financial analysts wanted to quickly evaluate the events and entities concerning this press release and wanted to make market predictions. We looked at an architecture to help you accomplish this. In the architecture shown in Figure 1.1, we spoke about how you can use AWS AI services such as Amazon Textract to extract text from the sample press release documents. Then, we saved all the text with utf-8 encoding in the Amazon S3 bucket for Amazon Comprehend entity or metadata extractions jobs.

We used an Amazon Comprehend Events job to extract entities and relationships between the entity. We have provided a walkthrough video link of the Comprehend Events feature in the Further reading section if you wish to learn more. We also provided two ways to configure Comprehend Events job...