What this book covers
Chapter 1, NLP in the Business Context and Introduction to AWS AI Services, introduces the NLP construct and the business value of using NLP, leading to an overview of the AWS AI stack along with the key NLP services.
Chapter 2, Introducing Amazon Textract, provides a detailed introduction to Amazon Textract, what its functions are, what business challenges it was created to solve, what features it has, what types of user requirements it can be applied to, and how easy it is to integrate Textract with other AWS services, such as AWS Lambda for building business applications.
Chapter 3, Introducing Amazon Comprehend, provides a detailed introduction to Amazon Comprehend, what its functions are, what business challenges it was created to solve, what features it has, what types of user requirements it can be applied to, and how easy it is to integrate Comprehend with other AWS services, such as AWS Lambda for building business applications.
Chapter 4, Automating Document Processing Workflows, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step-by-step guide on how to set up and run these examples along with access to the GitHub repository.
Chapter 5, Creating NLP Search, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step-by-step guide on how to set up and run these examples along with access to the GitHub repository.
Chapter 6, Using NLP to Improve Customer Service Efficiency, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 7, Understanding the Voice of Your Customer Analytics, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 8, Leveraging NLP to Monetize Your Media Content, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 9, Extracting Metadata from Financial Documents, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 10, Reducing Localization Costs with Machine Translation, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 11, Using Chatbots for Querying Documents, dives deep into the several types of use cases prevalent across industries that can benefit from NLP based on our collective experience and the usage trends we have observed. We will provide detailed code samples, a design and development approach, and a step by step guide on how to set up and run these examples along with access to the Github repository.
Chapter 12, AI and NLP in Healthcare, dives deep into the use case of how AWS NLP solutions can help achieve operational efficiency in healthcare with an automated claims adjunction use case.
Chapter 13, Improving the Accuracy of Document Processing Workflows, talks about why we need humans in the loop (HITLs) in document processing workflows, and how setting up HITL processes with Amazon Augmented AI (A2I) can help improve the accuracy of your existing document processing workflows with Amazon Textract.
Chapter 14, Auditing Named Entity Recognition Workflows, walks through an extension of the previous approach by including Amazon Comprehend for text-based insights, thereby demonstrating an end-to-end process for setting up an auditing workflow for your custom named entity recognition use cases.
Chapter 15, Classifying Documents and Setting up Human in the Loop for Active Learning, talks 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.
Chapter 16, Improving the Accuracy of PDF Batch Processing, tackles an operational need that has been around for a while and is ubiquitous, and yet organizations struggle to address it efficiently – known as PDF batch processing.
Chapter 17, Visualizing Insights from Handwritten Content, is all about how to visualize insights from text – that is, handwritten text – and make use of it to drive decision-making.
Chapter 18, Building Secure, Reliable, and Efficient NLP Solutions, reviews the best practices, techniques, and guidance on what makes a good NLP solution great.
