AWS Machine Learning Blog

Category: Amazon Comprehend

Build a system for catching adverse events in real-time using Amazon SageMaker and Amazon QuickSight

Social media platforms provide a channel of communication for consumers to talk about various products, including the medications they take. For pharmaceutical companies, monitoring and effectively tracking product performance provides customer feedback about the product, which is vital to maintaining and improving patient safety. However, when an unexpected medical occurrence resulting from a pharmaceutical product […]

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Translate and analyze text using SQL functions with Amazon Redshift, Amazon Translate, and Amazon Comprehend

You may have tables in your Amazon Redshift data warehouse or in your Amazon Simple Storage Service (Amazon S3) data lake full of records containing customer case notes, product reviews, and social media messages, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy thoughts […]

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Use the AWS Cloud for observational life sciences studies

In this post, we discuss how to use the AWS Cloud and its services to accelerate observational studies for life sciences customers. We provide a reference architecture for architects, business owners, and technology decision-makers in the life sciences industry to automate the processes in clinical studies. Observational studies lead the way in research, allowing you […]

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Custom document annotation for extracting named entities in documents using Amazon Comprehend

Intelligent document processing (IDP), as defined by IDC, is an approach by which unstructured content and structured data is analyzed and extracted for use in downstream applications. IDP involves document reading, categorization, and data extraction, by using AI’s processes of computer vision (CV), Optical Character Recognition (OCR), and natural language processing (NLP) on provided texts.[1] […]

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Extract custom entities from documents in their native format with Amazon Comprehend

Multiple industries such as finance, mortgage, and insurance face the challenge of extracting information from documents and taking a specific action to enable business processes. Intelligent document processing (IDP) helps extract information locked within documents that is important to business operations. Customers are always seeking new ways to use artificial intelligence (AI) to help them […]

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AWS is redefining how companies process documents in a digital world

Think about the last time you opened a bank account, applied for insurance, or refinanced your home. It was probably done on paper. The number of documents in a mortgage packet alone is over 100 pages long. What do you do with all that paper? For many companies across a variety of industries, including financial […]

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Announcing model improvements and lower annotation limits for Amazon Comprehend custom entity recognition

Amazon Comprehend is a natural language processing (NLP) service that provides APIs to extract key phrases, contextual entities, events, sentiment from unstructured text, and more. Entities refer to things in your document such as people, places, organizations, credit card numbers, and so on. But what if you want to add entity types unique to your […]

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How Daniel Wellington’s customer service department saved 99% on translation costs with Amazon Translate

This post is co-authored by Lezgin Bakircioglu, Innovation and Security Manager at Daniel Wellington. In their own words, “Daniel Wellington (DW) is a Swedish fashion brand founded in 2011. Since its inception, it has sold over 11 million watches and established itself as one of the fastest-growing and most coveted brands in the industry.” In […]

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Protect PII using Amazon S3 Object Lambda to process and modify data during retrieval

Regulatory mandates, audit requirements, and security policies often call for data visibility and granular data control while using Amazon Simple Storage Service (Amazon S3) for shared datasets. Because data on Amazon S3 is often accessible by multiple applications and teams, fine-grained access controls should be implemented to restrict privileged information such as personally identifiable information […]

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Segment paragraphs and detect insights with Amazon Textract and Amazon Comprehend

Many companies extract data from scanned documents containing tables and forms, such as PDFs. Some examples are audit documents, tax documents, whitepapers, or customer review documents. For customer reviews, you might be extracting text such as product reviews, movie reviews, or feedback. Further understanding of the individual and overall sentiment of the user base from […]

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