Amazon SageMaker Ground Truth Adds Data Labeling Workflow for Named Entity Recognition
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.
SageMaker Ground Truth now provides a built-in data labeling workflow for named entity recognition (NER). In the traditional sense, NER involves sifting through text data and locating noun phrases called “named entities”. Each of these named entities is then categorized with a label, such as ‘person’, ‘organization’, ‘brand’, etc. This use case can be extended more broadly to labeling longer spans of text and categorizing those sequences into any pre-specified labels.
You can refer to the Amazon SageMaker Ground Truth documentation for details, and also learn more in the blog post.