I’ve been working with text annotation lately and it’s funny how straightforward it seems until you actually try to keep things consistent across a dataset.
Some patterns I keep stumbling on are:
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annotation drift — people label the same sentence differently
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handling sentiment in edge cases
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deciding on granular categories vs broad ones
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ambiguous phrases that could fit multiple labels
I was thinking through how teams structure their text annotation workflows to deal with these issues — things like guidelines, reviewer calibration, and multi-step QA — and found a breakdown that explains some of the core ideas around how people handle these challenges:
https://aipersonic.com/text-annotation-services/
Not recommending anything here, just sharing a context that helped me frame the problem.
For folks who’ve done this at scale:
• What strategies help keep label consistency?
• Do you prefer smaller, detailed label sets or larger broad categories?
• How do you handle ambiguous text — rule first, or review later?
• Any tips for training reviewers quickly but effectively?
Curious to learn what actually works in real workflows.