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

The need for setting up HITL processes with document processing

In the previous chapters, we have discussed how you can use Amazon Textract and Amazon Comprehend to automate your existing document processing workflows with AWS AI services. We covered some of the key use cases such as using Comprehend to analyze SEC filing reports and using Textract to extract text from any document or quickly digitize any document. We also spoke about how these AI services provide a confidence score with each predicted text, word, line, or entity. Now, the questions that customers often ask are about how to improve these predictions and to make sure they are highly accurate. In most AI systems, it's either AI doing the automation process or it's only humans or manual processes.

The ideal scenario would be both humans and AI working together so that the results predicted by these AI systems can be reviewed by humans to make sure they are highly accurate. This applies to scenarios where...