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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

By : M, Premkumar Rangarajan
5 (21)
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Natural Language Processing with AWS AI Services

Natural Language Processing with AWS AI Services

5 (21)
By: 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)
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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

Creating NLP-powered smart search indexes

Every organization has lots of documents in the form of paper and in their archives too. The challenge is that these documents lie mostly in separate silos and not all in one place. So, for these organizations to make a business decision based on the hidden information in their siloed documents is extremely challenging. Some approaches these organizations take to make their documents searchable is putting the documents in a data lake. However, extracting meaningful information from these documents is another challenge as it would require a lot of NLP expertise, ML skills, and infrastructure to set that up. Even if you were able to extract insights from these documents, another challenge will then be setting up a scalable search solution.

In this section, we will address these challenges by using the AWS AI services we introduced in previous chapters and then talk about how they can be used to set up a centralized document store.

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