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

Introducing the localization use case

In the past few chapters, we looked at a variety of ways NLP can help us understand our customers better. We learned how we can build applications to detect sentiments, monetize content, detect unique entities, and understand context, references, and other analytics processes that help organizations gain important insights about their business. In this chapter, we will learn how to automate the process of translating website content into multiple languages. To illustrate this example, we'll assume that our fictitious banking corporation, LiveRight Holdings Private Limited, has decided to expand internationally to delight potential customers in Germany, Spain, and the cities Mumbai and Chennai in India. The launch date for these four pilot regions is coming up fast; that is, in the next 3 weeks. The expansions operations lead has escalated his concerns to senior management, stating that the IT teams may not be ready with the websites in the...