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Capability

Ability to automatically tag true content "aboutness"

The capability to predict subject tags (concepts) that a piece of content is actually "about" rather than purely predicting subject tags that have been mentioned.
Delivered by these Product Features
Understands true "Aboutness"
Understands true "Aboutness"
"Aboutness" is the actual, true focus of content - not just what is mentioned!
Involves these Technologies
Automated Content Tagging using Artificial Intelligence (AI)
The use of AI or Machine Learning (ML) to automatically predict content classifications. This is also known as "Auto-tagging" or "Auto-coding" (Healthcare applications) or "Auto-classification".
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Automated Content Tagging using Artificial Intelligence (AI)
After many enquiries about automated content tagging using AI, we have put some of our learnings and expertise, from 14 years of experience in this space, into the Auto-Tagging Playbook.
Matt Shearer
Matt Shearer
March 30, 2023
FAQs
What is "aboutness" in tagging terminology?
  • "Aboutness" refers to the true topic of a piece of media content (text / image / video /.. etc), and not just what it "mentions" or "depicts".
  • When using classic NLP, generic AI auto-tagging systems, or NER systems on text, you tend to get tag predictions that include all the things that are mentioned.
  • As a meta example, this FAQ mentions "NLP", "FAQ", "Media", "Mentions", "text", "image", "video" and more, but what it is really about is "aboutness" (and also "tagging")
  • A true "aboutness" auto-tagging system learns not what is mentioned in the text or seen in imagery, but what a subject matter expert determines that the content is about.
What's the difference between "about" and "mentions"?

In content tagging terminology:

  • "About" is used to describe the subject(s) (or "concepts") that a piece of content is truly about.
  • "Mentions" is used to describe the concepts that are mentioned, and this tends to be more verbose,
  • "About" tends to be more useful in driving discovery, engagement, and user journeys that carry meaning, whereas "Mentions" is useful for very detailed archive indexing.