AWS Machine Learning Blog
Category: Amazon Comprehend
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 […]
Custom document annotation for extracting named entities in documents using Amazon Comprehend
This blog was last reviewed and updated in June, 2022 to include code updates and fixes. 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 […]
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 […]
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 […]
Announcing model improvements and lower annotation limits for Amazon Comprehend custom entity recognition
Update August 3, 2022: Minimum requirements for training entity recognizers have been further reduced. You can now build a custom entity recognition model with as few as three documents and 25 annotations per entity type. Additional details available in the Amazon Comprehend Guidelines and quotas webpage and in the blog post announcing the limit reduction. […]
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 […]
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 […]
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 […]
Intelligent governance of document processing pipelines for regulated industries
Processing large documents like PDFs and static images is a cornerstone of today’s highly regulated industries. From healthcare information like doctor-patient visits and bills of health, to financial documents like loan applications, tax filings, research reports, and regulatory filings, these documents are integral to how these industries conduct business. The mechanisms by which these documents […]
Enforce VPC rules for Amazon Comprehend jobs and CMK encryption for custom models
You can now control the Amazon Virtual Private Cloud (Amazon VPC) and encryption settings for your Amazon Comprehend APIs using AWS Identity and Access Management (IAM) condition keys, and encrypt your Amazon Comprehend custom models using customer managed keys (CMK) via AWS Key Management Service (AWS KMS). IAM condition keys enable you to further refine […]