TAKT Website

What does Media Monitors do?

Media Monitors is the nation’s leading local monitoring company, serving the media and advertising industries with near real-time intelligence on broadcast TV, cable, radio, print and digital using an AI-powered engine.

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What was the Annotation Task?

Classify podcast transcriptions into different categories of ads based on their context, type and specificity. Ads were of the form of Direct Response, Live Broadcast, Promotion, etc. This functioned as the training dataset for Media Monitor’s ad relevance and targeting AI.

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Task Highlights

Complexity

Low

Accuracy

99%

Volume

504,000 data points

Price

$0.04 per data point

Task Type

Classification (5 Categories)

This is some text inside of a div block.

- Sacha Spitzer, Podcast Product Manager at Media Monitors

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Solution

Takt hired full time annotators with experience in marketing and sales who understood the context and application of the dataset. Conducted dataset fit tests as part of the hiring which ensured they understood the varying podcast lingo.
Concurrent training sessions where the transcription was reviewed along with the audio of the podcast helped annotators gain clarity of the dataset. Guidelines to simplify the classification categories into subcategories that were easily understandable reduced the annotator decision processing.
Daily review processes ensured that errors did not propagate and a mistake of one annotator was a learning for every annotator. Conducted 100% review for the 1st week, 75% for the 2nd week, 50% for the 3rd week and 25% for the 4th week.
Feedback loops helped flag issues and doubts during annotation rather than let annotators pick with uncertainty.
Hired 12 annotators with high dataset fit within the first week and trained each annotator in a 4 day period of intense feedback and review. Achieved a daily output of approximately 30,000 lines in the 3rd week which ensured timely completion of the task.

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