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

Understanding Amazon Comprehend and Amazon Comprehend Medical

In this section, we will talk about the challenges associated with setting up ML (ML) preprocessing for NLP (NLP). Then, we will talk about how Amazon Comprehend and Amazon Comprehend Medical can help solve these pain points. Finally, we will talk about how you can use Amazon Comprehend to analyze the extracted text from documents by using Amazon Textract to extract the data.

Challenges associated with setting up ML preprocessing for NLP

Some of the key challenges while setting up NLP preprocessing are that documents can be semi-structured, unstructured, or can be in various languages. Once you have a large amount of unstructured data, you would probably like to extract insights from the data using some NLP techniques for most common use cases such as sentiment analysis, text classification, NER (NER), machine translation, and topic modeling.

Figure 3.1 – NLP modeling

Figure 3.1 – NLP modeling

The challenge...