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

Using Comprehend custom classification with human in the loop for active learning

Amazon Comprehend provides the capability to classify the data using Amazon Comprehend AutoML and bring your own custom training dataset. You can easily accomplish a lot with the Amazon Comprehend custom classification feature as it requires fewer documents to train Comprehend AutoML models. You are spending less time labeling the dataset and then worrying about setting up infrastructure or choosing the right algorithm.

You can use Amazon Comprehend custom classification for a variety of use cases, such as classifying documents based on type, classifying news articles, or classifying movies based on type.

The fictitious company LiveRight pvt ltd wants to classify the documents submitted by the customers, such as whether the document submitted is an ID or a bank statement, even before analyzing the data inside the document. Moreover, if you are using a classification model to classify the documents...