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

Extracting metadata from financial documents

In this section, we will talk about a use case where LiveRight Holdings private limited is attempting to acquire AwakenLife Pvt Ltd. They are going to do a press release soon and financial analysts are curious to identify the important metadata such as the acquisition date, amount, organization, and so forth so that they can act according to the market. LiveRight analyzed the Amazon Whole Foods merger to determine what it can learn and how metadata extraction will be useful for its due diligence. We will use the Amazon Whole Foods merger sample dataset to understand how you can perform metadata extraction using the preceding architecture:

Figure 9.1 – Metadata extraction architecture

Figure 9.1 – Metadata extraction architecture

In this architecture, we will start with large financial documents for extracting metadata. We will show you how you can use Amazon Textract batch processing jobs to extract data from this large document and save this extracted...