The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth. Annotators use this tool to label text with named entities and link their relationships, thereby building a dataset for training state-of-the-art natural language processing (NLP) machine learning (ML) models. Most importantly, this is now publicly available to all AWS customers.
Customer Use Case: Booking.com
Booking.com is one of the world’s leading online travel platforms. Understanding what customers are saying about the company’s 28 million+ property listings on the platform is essential for maintaining a top-notch customer experience. Previously, Booking.com could only utilize traditional sentiment analysis to interpret customer-generated reviews at scale. Looking to upgrade the specificity of these interpretations, Booking.com recently turned to the MLSL for help with building a custom annotated dataset for training an aspect-based sentiment analysis model.
Traditional sentiment analysis is the

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