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Relevance 8/10Text and NLPBeginner6 min read
Intent Classification
Intent classification labels the underlying user goal in text or voice requests.
Why it matters for annotators
Intent quality drives chatbot routing and assistant behavior.
Visual mental model
Utterance -> intent class.
Examples (bad vs good)
Scenario: Real annotation scenario involving Intent Classification
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.