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

Summary

In this chapter, we covered how you can set up a text analytics solution with your existing social media analytics workflow. We gave a specific example of using the Yelp reviews dataset and using serverless ETL with NLP using Amazon Comprehend to set up a quick visual dashboard using Amazon QuickSight. We also covered ad hoc SQL analytics using Amazon Athena to understand the voice or sentiment of the majority of your users using some easy SQL queries. This solution can be implemented with any social media integration, such as Twitter, Reddit, and Facebook, in batch or real-time mode.

In the case of a real-time setup, you would integrate Kinesis Data Firehose to have near real-time streaming tweets or social media feeds in this proposed workflow or architecture. Check out the Further reading section for a really cool AI-driven social media dashboard to implement this architecture at scale.

Another approach you can take in terms of document automation is to have Amazon...