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

Chapter 15: Classifying Documents and Setting up Human in the Loop for Active Learning

In the last chapter, we covered how you can use Amazon Comprehend Custom Entity to extract business entities from your documents, and we showed you how you can use humans in the loop with Amazon Augmented AI (A2I) to augment or improve entity predictions. Lastly, we showed you how you can retrain the Comprehend custom entity model with an augmented dataset to improve accuracy using Amazon A2I.

In this chapter, we will talk about how you can use Amazon Comprehend custom classification to classify documents and then how you can set up active learning feedback with your custom classification model using Amazon A2I.

We will be covering the following topics in this chapter:

  • Using comprehend custom classification with human in the loop for active learning
  • Building the document classification workflow