Creating a Robust Tagging Framework with NLP
Content availability is paramount to both research creators and providers as they seek to maintain mind share with clients. A robust framework that applies accurate tags to the most prevalent themes and entities within research documents promotes the discoverability and timely consumption of relevant information.
However, today’s manual and legacy automated tagging approaches fail to address these needs comprehensively. One example is third-party research content that is insufficiently and inconsistently tagged, leading to challenges in the discovery and timely consumption of content.
The Thematic & Entity Tagging Use Case discusses the significant business implications of using a manual or limited tagging methodology. It then shows how NLP models can be used to address these challenges with a review of Amenity’s thematic and entity tagging capabilities.
Access this use case to discover how Amenity's NLP-based system fully leverages research text, not only parsing with a high degree of accuracy, but also adding context and metadata to each extraction. Amenity’s robust tagging framework specializes in classifying thematic and topical content, as well as offers a more detailed targeting of asset classes and other entities than typical tagging platforms.
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